Consulting Engagement with the Chilean Government¶
Representatives of Chilean government (the Ministry of Economic Development and Tourism of Chile) hired a consulting company, trying to improve the tourism industry in the country. Government officials provided some existing data (messy as always!) and expect meaninful insights and recommendations.
Project objective:
Based on the data associated to the 15 regions of Chile under the 5 dimensions considered, you are expected to deliver a report that will include the three sections outlined below:
Perform a Principal Component Analysis (PCA) for the dataset provided.
Based on the values obtained for each region under each of the ten dimensions, come up with an overall tourism competitiveness ranking for all regions (as presented in the example available in the previous module).
In [7]:
# Import useful libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
pass
In [8]:
# Set additional parameters for charts and tables
# Remove column display limits
pd.set_option('display.max_columns', None)
# pd.set_option('display.height', None)
pd.set_option('display.max_rows', None)
# pd.set_option('display.width', None)
plt.rcParams['figure.figsize'] = [15, 10]
sns.set_style("white")
1. Data Importing and Data Cleaning
In [9]:
# Read data in csv format, using encoding, and read the third row as column names
chile_data_1 = pd.read_csv('Tourism Chile D1 - D5.csv', encoding = 'ISO-8859-1', header = 3)
chile_data_1.drop(chile_data_1.iloc[:, 0:2], inplace = True, axis = 1)
In [10]:
# Print
chile_data_1.head()
Out[10]:
| VARIABLE | CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR | NUMBER OF CULTURAL CENTERS | WORLD CULTURAL HERITAGE SITES | NUMBER OF ARCHEOLOGICAL SITES | NATIONAL MONUMENTS | MUSEUMS | % OF POPULATION THAT ATTENDS MUSEUMS | THEATERS | NUMBER OF THEATER PLAYS PER YEAR | LIBRARIES | GALERIES | % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP | NUMBER OF EXHIBITS | ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR | MAJOR SPORTS EVENTS PER YEAR | OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS | ARTWORK SITES | POPULAR ARCHITECTURE SITES | HISTORICAL SITES | LOCAL MARKETS | CULTURAL SITES LEVEL III (INTERNATIONAL) | CULTURA SITES LEVEL II (NATIONAL) | CULTURAL SITES LEVEL I (LOCAL) | HERITAGE ARCHITECTURAL HOUSES | % OF LAND THAT CORRESPONDS TO FORESTS | NATIONAL PROTECTED SITES (%) | % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS | TOXIC WASTE DISPOSAL (TONS/100 hab.) | NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED | ENVIRONMENTAL ISSUES PER MILLION HABITANTS | NUMBER OF BEACHES AND BEACH RESORTS | LAND AFFECTED BY WILDFIRES | NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) | NATURAL PROTECTED SITES LEVEL II (NATIONAL) | RIVERS, LAKES AND WATERFALLS | MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS | GEISERS AND THERMAL CENTERS | PIERS AND SEASHORES | GLACIERS AND WINTER VACATION LOCATIONS | VALLEYS | DESERTS AND DUNES | ISLANDS AND PENINSULAS | PALEONTOLOGY SITES | HIKING TRAILS | PRESERVED SITES | SEASHORE PROTECTED SITES | BIOSHPERE RESERVES | % AVAILABLE WORKFORCE | % POPULATION ORIENTED TOWARDS TOURISM | AVERAGE MONTHLY INCOME (CHILEAN PESOS) | 5 POPULATION WITH PRIMARY EDUCATION | % POPULATION WITH SECONDARY EDUCATION | AVERAGE NUMBER OF YEARS STUDYING | HIGHER EDUCATION AND TECHNICAL INSTITUTIONS | TOURISM-ORIENTED INSTITUTIONS | NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS | AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS | DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) | CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS | % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR | ROOMS PER 1000 HABITANTS | NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. | TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) | AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR | AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND | NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION | NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS | TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS | TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) | NATIONAL TOURISTS ARRIVALS | INTERNATIONAL TOURISTS ARRIVALS | NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY | DENSITY OF AIRPORTS | DENSITY OF ROADS AND HIGHWAYS | % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) | NUMBER OF VEHICLES | VISITORS TO PROTECTED SITES | NUMBER OF CRUISES THAT ARRIVE PER YEAR | TOURIST'S ARRIVALS THROUGH BORDER LINES | SECONDARY ROADS (KMS) | NUMBER OF INTERNATIONAL BORDER GATES | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Arica y Parinacota | 31.0 | 2 | 0 | 30 | 28 | 4 | 22.1 | 2 | 105 | 12 | 2 | 25.4 | 1 | 0 | 0 | 0 | 4 | 32 | 4 | 3 | 9 | 32 | 7 | 59 | - | 21.9 | 0.46 | 58.00 | 1 | 0.00 | 5 | - | 13 | 13 | 4 | 6 | 6 | 2 | 0 | 8 | 2 | 0 | 0 | 2 | - | 0 | 1 | 42.556 | 0.88 | $ 293,648 | 94.1 | 83.8 | 11.1 | 53.0 | 2 | 193 | 11 | 20.038 | 356 | 23.69 | 18.74 | 6,544 | 33.01 | 37.0 | 2.3 | 5 | 3 | 0.0 | 0 | 97,454 | 34,186 | 1,151,575 | 5.2730 | 2.129 | 17.35 | 45,248 | 15,045 | 13 | 167,211 | 4.67 | 3 |
| 1 | Tarapacá | 0.0 | 5 | 1 | 13 | 73 | 5 | 20.8 | 2 | 178 | 12 | 1 | 12.6 | 1 | 0 | 0 | 0 | 0 | 34 | 10 | 1 | 6 | 34 | 12 | 0 | 0.2 | 9.1 | 0.03 | 76.03 | 1 | 0.00 | 16 | - | 2 | 6 | 1 | 1 | 7 | 5 | 0 | 5 | 6 | 4 | 2 | 3 | 5 | 0 | 0 | 68.563 | 1.45 | $ 381,466 | 91.7 | 66.7 | 10.7 | 42.0 | 5 | 255 | 19 | 22.180 | 380 | 23.03 | 22.17 | 11,108 | 41.43 | 42.8 | 2.1 | 5 | 10 | 0.0 | 11 | 235,365 | 40,919 | 19,560 | 4.1850 | 4.021 | 15.90 | 81,182 | 17,161 | 0 | 434,727 | 184.10 | 1 |
| 2 | Antofagasta | 1.0 | 9 | 0 | 28 | 81 | 16 | 27.4 | 8 | 203 | 15 | 2 | 5.7 | 2 | 1 | 0 | 3 | 1 | 24 | 37 | 0 | 4 | 31 | 26 | 63 | - | 2.8 | 0.03 | 39.56 | 2 | 0.00 | 22 | - | 5 | 28 | 8 | 13 | 3 | 14 | 1 | 6 | 10 | 6 | 4 | 0 | 5 | 0 | 0 | 54.486 | 1.08 | $ 475,866 | 91.7 | 66.7 | 10.6 | 40.0 | 7 | 529 | 47 | 20.446 | 184 | 24.55 | 22.76 | 19,920 | 35.02 | 44.6 | 1.8 | 2 | 10 | 17.0 | 15 | 413,922 | 84,195 | 22,898 | 2.0244 | 3.332 | 52.25 | 112,607 | 315,888 | 0 | 115,100 | 23.00 | 5 |
| 3 | Atacama | 8.0 | 10 | 0 | 8 | 35 | 7 | 20.0 | 0 | 144 | 12 | 0 | 7.5 | 0 | 0 | 0 | 0 | 0 | 18 | 5 | 0 | 2 | 29 | 7 | 33 | - | 2.0 | 0.02 | 40.76 | 0 | 3.93 | 17 | NaN | 7 | 23 | 2 | 7 | 2 | 10 | 0 | 3 | 13 | 1 | 3 | 2 | 5 | 1 | 0 | 51.844 | 0.94 | $ 379,971 | 89.1 | 73.2 | 10.3 | 47.0 | 1 | 108 | 16 | 18.479 | 578 | 19.43 | 18.49 | 9,674 | 29.25 | 36.0 | 1.9 | 1 | 3 | 0.0 | 15 | 168,508 | 14,222 | 2,416 | 0.0000 | 3.635 | 24.39 | 55,812 | 24,873 | 0 | 2,552 | 11.33 | 2 |
| 4 | Coquimbo | 23.0 | 7 | 0 | 2 | 52 | 7 | 18.3 | 4 | 80 | 22 | 2 | 1.7 | 0 | 1 | 0 | 7 | 1 | 69 | 2 | 1 | 0 | 29 | 27 | 97 | 0.8 | 0.4 | 0.35 | 25.45 | 1 | 0.00 | 37 | 1.50 | 4 | 16 | 7 | 0 | 1 | 21 | 0 | 18 | 0 | 9 | 1 | 0 | 5 | 2 | 1 | 56.498 | 0.92 | $ 338,014 | 92.7 | 71.2 | 9.7 | 43.0 | 7 | 870 | 85 | 6.963 | 1074 | 15.50 | 15.63 | 24,346 | 12.00 | 35.2 | 2.8 | 4 | 9 | 383.9 | 750 | 205,850 | 25,803 | 2,786 | 0.0000 | 1.912 | 283.37 | 116,263 | 57,234 | 0 | 15,265 | 228.58 | 1 |
In [11]:
# Remove the last row with aggregated Total data
chile_data_1 = chile_data_1[:-1]
In [12]:
chile_data_1
Out[12]:
| VARIABLE | CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR | NUMBER OF CULTURAL CENTERS | WORLD CULTURAL HERITAGE SITES | NUMBER OF ARCHEOLOGICAL SITES | NATIONAL MONUMENTS | MUSEUMS | % OF POPULATION THAT ATTENDS MUSEUMS | THEATERS | NUMBER OF THEATER PLAYS PER YEAR | LIBRARIES | GALERIES | % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP | NUMBER OF EXHIBITS | ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR | MAJOR SPORTS EVENTS PER YEAR | OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS | ARTWORK SITES | POPULAR ARCHITECTURE SITES | HISTORICAL SITES | LOCAL MARKETS | CULTURAL SITES LEVEL III (INTERNATIONAL) | CULTURA SITES LEVEL II (NATIONAL) | CULTURAL SITES LEVEL I (LOCAL) | HERITAGE ARCHITECTURAL HOUSES | % OF LAND THAT CORRESPONDS TO FORESTS | NATIONAL PROTECTED SITES (%) | % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS | TOXIC WASTE DISPOSAL (TONS/100 hab.) | NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED | ENVIRONMENTAL ISSUES PER MILLION HABITANTS | NUMBER OF BEACHES AND BEACH RESORTS | LAND AFFECTED BY WILDFIRES | NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) | NATURAL PROTECTED SITES LEVEL II (NATIONAL) | RIVERS, LAKES AND WATERFALLS | MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS | GEISERS AND THERMAL CENTERS | PIERS AND SEASHORES | GLACIERS AND WINTER VACATION LOCATIONS | VALLEYS | DESERTS AND DUNES | ISLANDS AND PENINSULAS | PALEONTOLOGY SITES | HIKING TRAILS | PRESERVED SITES | SEASHORE PROTECTED SITES | BIOSHPERE RESERVES | % AVAILABLE WORKFORCE | % POPULATION ORIENTED TOWARDS TOURISM | AVERAGE MONTHLY INCOME (CHILEAN PESOS) | 5 POPULATION WITH PRIMARY EDUCATION | % POPULATION WITH SECONDARY EDUCATION | AVERAGE NUMBER OF YEARS STUDYING | HIGHER EDUCATION AND TECHNICAL INSTITUTIONS | TOURISM-ORIENTED INSTITUTIONS | NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS | AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS | DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) | CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS | % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR | ROOMS PER 1000 HABITANTS | NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. | TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) | AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR | AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND | NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION | NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS | TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS | TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) | NATIONAL TOURISTS ARRIVALS | INTERNATIONAL TOURISTS ARRIVALS | NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY | DENSITY OF AIRPORTS | DENSITY OF ROADS AND HIGHWAYS | % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) | NUMBER OF VEHICLES | VISITORS TO PROTECTED SITES | NUMBER OF CRUISES THAT ARRIVE PER YEAR | TOURIST'S ARRIVALS THROUGH BORDER LINES | SECONDARY ROADS (KMS) | NUMBER OF INTERNATIONAL BORDER GATES | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Arica y Parinacota | 31.0 | 2 | 0 | 30 | 28 | 4 | 22.1 | 2 | 105 | 12 | 2 | 25.4 | 1 | 0 | 0 | 0 | 4 | 32 | 4 | 3 | 9 | 32 | 7 | 59 | - | 21.9 | 0.46 | 58.00 | 1 | 0.00 | 5 | - | 13 | 13 | 4 | 6 | 6 | 2 | 0 | 8 | 2 | 0 | 0 | 2 | - | 0 | 1 | 42.556 | 0.88 | $ 293,648 | 94.1 | 83.8 | 11.1 | 53.0 | 2 | 193 | 11 | 20.038 | 356 | 23.69 | 18.74 | 6,544 | 33.01 | 37.0 | 2.3 | 5 | 3 | 0.0 | 0 | 97,454 | 34,186 | 1,151,575 | 5.2730 | 2.129 | 17.35 | 45,248 | 15,045 | 13 | 167,211 | 4.67 | 3 |
| 1 | Tarapacá | 0.0 | 5 | 1 | 13 | 73 | 5 | 20.8 | 2 | 178 | 12 | 1 | 12.6 | 1 | 0 | 0 | 0 | 0 | 34 | 10 | 1 | 6 | 34 | 12 | 0 | 0.2 | 9.1 | 0.03 | 76.03 | 1 | 0.00 | 16 | - | 2 | 6 | 1 | 1 | 7 | 5 | 0 | 5 | 6 | 4 | 2 | 3 | 5 | 0 | 0 | 68.563 | 1.45 | $ 381,466 | 91.7 | 66.7 | 10.7 | 42.0 | 5 | 255 | 19 | 22.180 | 380 | 23.03 | 22.17 | 11,108 | 41.43 | 42.8 | 2.1 | 5 | 10 | 0.0 | 11 | 235,365 | 40,919 | 19,560 | 4.1850 | 4.021 | 15.90 | 81,182 | 17,161 | 0 | 434,727 | 184.10 | 1 |
| 2 | Antofagasta | 1.0 | 9 | 0 | 28 | 81 | 16 | 27.4 | 8 | 203 | 15 | 2 | 5.7 | 2 | 1 | 0 | 3 | 1 | 24 | 37 | 0 | 4 | 31 | 26 | 63 | - | 2.8 | 0.03 | 39.56 | 2 | 0.00 | 22 | - | 5 | 28 | 8 | 13 | 3 | 14 | 1 | 6 | 10 | 6 | 4 | 0 | 5 | 0 | 0 | 54.486 | 1.08 | $ 475,866 | 91.7 | 66.7 | 10.6 | 40.0 | 7 | 529 | 47 | 20.446 | 184 | 24.55 | 22.76 | 19,920 | 35.02 | 44.6 | 1.8 | 2 | 10 | 17.0 | 15 | 413,922 | 84,195 | 22,898 | 2.0244 | 3.332 | 52.25 | 112,607 | 315,888 | 0 | 115,100 | 23.00 | 5 |
| 3 | Atacama | 8.0 | 10 | 0 | 8 | 35 | 7 | 20.0 | 0 | 144 | 12 | 0 | 7.5 | 0 | 0 | 0 | 0 | 0 | 18 | 5 | 0 | 2 | 29 | 7 | 33 | - | 2.0 | 0.02 | 40.76 | 0 | 3.93 | 17 | NaN | 7 | 23 | 2 | 7 | 2 | 10 | 0 | 3 | 13 | 1 | 3 | 2 | 5 | 1 | 0 | 51.844 | 0.94 | $ 379,971 | 89.1 | 73.2 | 10.3 | 47.0 | 1 | 108 | 16 | 18.479 | 578 | 19.43 | 18.49 | 9,674 | 29.25 | 36.0 | 1.9 | 1 | 3 | 0.0 | 15 | 168,508 | 14,222 | 2,416 | 0.0000 | 3.635 | 24.39 | 55,812 | 24,873 | 0 | 2,552 | 11.33 | 2 |
| 4 | Coquimbo | 23.0 | 7 | 0 | 2 | 52 | 7 | 18.3 | 4 | 80 | 22 | 2 | 1.7 | 0 | 1 | 0 | 7 | 1 | 69 | 2 | 1 | 0 | 29 | 27 | 97 | 0.8 | 0.4 | 0.35 | 25.45 | 1 | 0.00 | 37 | 1.50 | 4 | 16 | 7 | 0 | 1 | 21 | 0 | 18 | 0 | 9 | 1 | 0 | 5 | 2 | 1 | 56.498 | 0.92 | $ 338,014 | 92.7 | 71.2 | 9.7 | 43.0 | 7 | 870 | 85 | 6.963 | 1074 | 15.50 | 15.63 | 24,346 | 12.00 | 35.2 | 2.8 | 4 | 9 | 383.9 | 750 | 205,850 | 25,803 | 2,786 | 0.0000 | 1.912 | 283.37 | 116,263 | 57,234 | 0 | 15,265 | 228.58 | 1 |
| 5 | Valparaíso | 14.0 | 37 | 2 | 24 | 161 | 12 | 25.7 | 7 | 322 | 56 | 7 | 3.2 | 3 | 3 | 1 | 4 | 2 | 48 | 21 | 4 | 46 | 90 | 36 | 720 | 6.5 | 2.7 | 2.11 | 38.46 | 13 | 0.00 | 59 | 6.64 | 13 | 71 | 3 | 0 | 3 | 14 | 3 | 22 | 1 | 0 | 0 | 14 | 5 | 3 | 2 | 53.534 | 0.99 | $ 311,264 | 92.9 | 73.5 | 10.6 | 27.0 | 21 | 3,949 | 316 | 12.209 | 3661 | 17.21 | 13.39 | 44,504 | 21.18 | 29.7 | 2.0 | 14 | 27 | 920.0 | 257 | 430,436 | 106,915 | 317,309 | 0.6494 | 0.197 | 280.95 | 316,618 | 146,161 | 31 | 436,195 | 93.86 | 1 |
| 6 | Metropolitana | 4.0 | 56 | 0 | 1 | 404 | 35 | 22.5 | 70 | 6558 | 127 | 25 | 4.1 | 30 | 0 | 0 | 3 | 2 | 5 | 9 | 4 | 4 | 85 | 112 | 274 | 6.9 | 0.9 | 7.25 | 47.03 | 16 | 0.00 | 0 | 0.04 | 3 | 3 | 0 | 3 | 4 | 0 | 5 | 9 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 55.901 | 6.06 | $ 421,484 | 93.1 | 70.5 | 11.2 | 19.0 | 30 | 12,881 | 1261 | 4.834 | 15221 | 38.13 | 4.33 | 43,634 | 9.32 | 59.8 | 2.2 | 15 | 133 | 754.2 | 236 | 448,887 | 765,681 | 1,091,111 | 1.2810 | 0.731 | 541.32 | 1,306,140 | 83,459 | 0 | 1,147,039 | 248.50 | 1 |
| 7 | O'Higgins | 32.0 | 13 | 1 | 9 | 67 | 6 | 21.9 | 13 | 251 | 36 | 0 | 1.9 | 1 | 7 | 2 | 3 | 16 | 29 | 3 | 1 | 0 | 28 | 15 | 229 | 11.3 | 2.8 | 0.92 | 27.15 | 4 | 1.28 | 12 | 2.47 | 5 | 22 | 1 | 3 | 2 | 2 | 7 | 5 | 2 | 1 | 1 | 7 | 5 | 0 | 0 | 54.662 | 0.62 | $ 308,068 | 95.5 | 65.0 | 9.5 | 23.0 | 7 | 352 | 15 | 9.992 | 2833 | 17.46 | 8.01 | 14,526 | 6.34 | 15.4 | 2.2 | 2 | 7 | 10.0 | 0 | 73,478 | 7,066 | - | 0.0000 | 1.352 | 134.50 | 164,204 | 11,073 | 0 | 0 | 62.12 | 0 |
| 8 | Maule | 29.0 | 17 | 0 | 1 | 54 | 7 | 11.1 | 9 | 657 | 29 | 1 | 1.6 | 1 | 7 | 0 | 0 | 10 | 27 | 4 | 3 | 0 | 37 | 28 | 19 | 12.7 | 0.6 | 0.41 | 32.86 | 5 | 0.00 | 18 | 0.82 | 0 | 20 | 23 | 12 | 7 | 5 | 6 | 13 | 3 | 0 | 0 | 9 | 5 | 0 | 0 | 48.164 | 0.24 | $ 244,231 | 93.4 | 73.1 | 9.0 | 24.0 | 4 | 364 | 37 | 8.700 | 2898 | 14.49 | 6.10 | 12,278 | 8.67 | 28.4 | 2.0 | 3 | 14 | 0.0 | 0 | 167,293 | 8,935 | 3,942 | 0.0000 | 1.480 | 197.65 | 185,728 | 64,500 | 0 | 853 | 232.90 | 2 |
| 9 | Biobío | 5.0 | 32 | 0 | 0 | 59 | 20 | 18.9 | 12 | 488 | 63 | 7 | 3.9 | 10 | 0 | 0 | 0 | 4 | 13 | 23 | 2 | 0 | 10 | 54 | 135 | 20.7 | 2.8 | 0.96 | 31.68 | 2 | 1.07 | 25 | 0.73 | 3 | 7 | 19 | 3 | 2 | 4 | 3 | 2 | 0 | 1 | 8 | 0 | 5 | 0 | 0 | 48.651 | 1.23 | $ 290,367 | 92.7 | 71.1 | 9.9 | 23.0 | 17 | 4,023 | 320 | 1.612 | 2334 | 16.04 | 4.93 | 20,802 | 7.89 | 36.0 | 1.9 | 5 | 12 | 25.0 | 9 | 393,481 | 31,409 | 1,546 | 0.0000 | 1.021 | 400.89 | 312,085 | 49,841 | 0 | 828 | 642.26 | 1 |
| 10 | Araucanía | 18.0 | 13 | 0 | 2 | 96 | 5 | 13.2 | 8 | 179 | 58 | 4 | 30.1 | 0 | 5 | 3 | 0 | 13 | 16 | 6 | 10 | 0 | 12 | 31 | 20 | 29.4 | 9.6 | 0.43 | 36.79 | 6 | 0.00 | 25 | 0.16 | 22 | 79 | 51 | 10 | 17 | 3 | 7 | 10 | 0 | 1 | 3 | 23 | 5 | 0 | 1 | 52.282 | 2.30 | $ 251,081 | 94.5 | 72.0 | 9.1 | 29.0 | 8 | 709 | 67 | 9.085 | 709 | 11.21 | 9.90 | 26,140 | 16.09 | 37.4 | 2.5 | 15 | 7 | 24.0 | 25 | 200,377 | 38,524 | 130,713 | 0.0000 | 1.615 | 236.97 | 124,956 | 228,921 | 0 | 112,246 | 469.74 | 3 |
| 11 | Los Ríos | 3.0 | 9 | 0 | 1 | 33 | 8 | 16.2 | 3 | 94 | 12 | 2 | 16.7 | 2 | 1 | 0 | 1 | 0 | 10 | 5 | 2 | 0 | 10 | 19 | 55 | 46.1 | 6.9 | 0.31 | 35.41 | 0 | 0.00 | 10 | 0.14 | 2 | 31 | 13 | 3 | 14 | 4 | 0 | 1 | 0 | 5 | 1 | 0 | - | 0 | 1 | 47.218 | 0.41 | $ 268,648 | 93.6 | 64.8 | 9.3 | 31.0 | 4 | 444 | 58 | 8.698 | 868 | 17.28 | 18.91 | 12,846 | 11.81 | 37.9 | 1.7 | 9 | 1 | 54.1 | 0 | 113,900 | 16,070 | 11,145 | 0.0000 | 2.019 | 133.02 | 51,811 | 376 | 7 | 0 | 42.67 | 1 |
| 12 | Los Lagos | 1.0 | 24 | 1 | 0 | 64 | 11 | 19.8 | 8 | 272 | 31 | 1 | 20.8 | 2 | 0 | 0 | 0 | 0 | 38 | 3 | 3 | 0 | 57 | 30 | 94 | 56.3 | 15.9 | 0.18 | 34.77 | 1 | 0.00 | 18 | 0.00 | 12 | 41 | 17 | 10 | 9 | 10 | 3 | 6 | 0 | 31 | 0 | 0 | 7 | 3 | 1 | 54.555 | 14.97 | $ 291,431 | 92.9 | 66.7 | 9.1 | 31.0 | 10 | 856 | 72 | 20.928 | 2672 | 14.08 | 23.97 | 35,548 | 23.82 | 31.6 | 1.8 | 27 | 13 | 54.1 | 48 | 275,043 | 105,048 | 259,411 | 10.9299 | 1.893 | 135.48 | 129,882 | 486,725 | 28 | 192,977 | 660.16 | 3 |
| 13 | Aysén | 0.0 | 4 | 0 | 2 | 17 | 0 | 14.8 | 0 | 49 | 12 | 0 | 21.8 | 0 | 0 | 0 | 0 | 0 | 24 | 2 | 0 | 1 | 20 | 10 | 0 | 44.4 | 39.4 | 0.02 | 43.89 | 0 | 0.00 | 0 | 0.01 | 21 | 93 | 25 | 8 | 6 | 5 | 23 | 3 | 0 | 8 | 3 | 6 | 6 | 0 | 2 | 61.016 | 1.05 | $ 418,044 | 95.3 | 68.5 | 9.5 | 66.0 | 3 | 64 | 6 | 67.765 | 83 | 6.83 | 37.60 | 8,020 | 23.06 | 21.7 | 1.7 | 0 | 1 | 0.0 | 19 | 28,584 | 10,763 | 90,876 | 0.0000 | 4.089 | 3.70 | 19,348 | 32,030 | 0 | 56,201 | 5.00 | 4 |
| 14 | Magallanes y Antártica | 0.0 | 3 | 0 | 2 | 45 | 6 | 27.7 | 2 | 4 | 14 | 0 | 22.7 | 3 | 0 | 0 | 0 | 2 | 16 | 16 | 0 | 2 | 24 | 11 | 34 | 20.2 | 57.3 | 0.04 | 40.58 | 3 | 0.00 | 0 | 0.01 | 19 | 62 | 23 | 10 | 0 | 17 | 20 | 10 | 0 | 0 | 1 | 1 | 5 | 1 | 1 | 51.872 | 0.06 | $ 572,203 | 93.6 | 77.6 | 10.2 | 60.0 | 6 | 380 | 55 | 137.244 | 222 | 12.94 | 41.53 | 12,436 | 67.76 | 37.1 | 2.0 | 5 | 3 | 0.0 | 12 | 84,034 | 109,092 | 262,190 | 0.1650 | 3.632 | 6.02 | 46,336 | 283,629 | 18 | 388,598 | 0.00 | 5 |
In [13]:
# Print dimensions
chile_data_1.shape
Out[13]:
(15, 82)
In [14]:
# Rename the first column
chile_data_1 = chile_data_1.rename(columns={'VARIABLE': 'Region'})
In [15]:
# Print
chile_data_1
Out[15]:
| Region | CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR | NUMBER OF CULTURAL CENTERS | WORLD CULTURAL HERITAGE SITES | NUMBER OF ARCHEOLOGICAL SITES | NATIONAL MONUMENTS | MUSEUMS | % OF POPULATION THAT ATTENDS MUSEUMS | THEATERS | NUMBER OF THEATER PLAYS PER YEAR | LIBRARIES | GALERIES | % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP | NUMBER OF EXHIBITS | ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR | MAJOR SPORTS EVENTS PER YEAR | OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS | ARTWORK SITES | POPULAR ARCHITECTURE SITES | HISTORICAL SITES | LOCAL MARKETS | CULTURAL SITES LEVEL III (INTERNATIONAL) | CULTURA SITES LEVEL II (NATIONAL) | CULTURAL SITES LEVEL I (LOCAL) | HERITAGE ARCHITECTURAL HOUSES | % OF LAND THAT CORRESPONDS TO FORESTS | NATIONAL PROTECTED SITES (%) | % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS | TOXIC WASTE DISPOSAL (TONS/100 hab.) | NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED | ENVIRONMENTAL ISSUES PER MILLION HABITANTS | NUMBER OF BEACHES AND BEACH RESORTS | LAND AFFECTED BY WILDFIRES | NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) | NATURAL PROTECTED SITES LEVEL II (NATIONAL) | RIVERS, LAKES AND WATERFALLS | MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS | GEISERS AND THERMAL CENTERS | PIERS AND SEASHORES | GLACIERS AND WINTER VACATION LOCATIONS | VALLEYS | DESERTS AND DUNES | ISLANDS AND PENINSULAS | PALEONTOLOGY SITES | HIKING TRAILS | PRESERVED SITES | SEASHORE PROTECTED SITES | BIOSHPERE RESERVES | % AVAILABLE WORKFORCE | % POPULATION ORIENTED TOWARDS TOURISM | AVERAGE MONTHLY INCOME (CHILEAN PESOS) | 5 POPULATION WITH PRIMARY EDUCATION | % POPULATION WITH SECONDARY EDUCATION | AVERAGE NUMBER OF YEARS STUDYING | HIGHER EDUCATION AND TECHNICAL INSTITUTIONS | TOURISM-ORIENTED INSTITUTIONS | NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS | AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS | DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) | CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS | % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR | ROOMS PER 1000 HABITANTS | NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. | TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) | AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR | AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND | NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION | NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS | TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS | TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) | NATIONAL TOURISTS ARRIVALS | INTERNATIONAL TOURISTS ARRIVALS | NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY | DENSITY OF AIRPORTS | DENSITY OF ROADS AND HIGHWAYS | % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) | NUMBER OF VEHICLES | VISITORS TO PROTECTED SITES | NUMBER OF CRUISES THAT ARRIVE PER YEAR | TOURIST'S ARRIVALS THROUGH BORDER LINES | SECONDARY ROADS (KMS) | NUMBER OF INTERNATIONAL BORDER GATES | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Arica y Parinacota | 31.0 | 2 | 0 | 30 | 28 | 4 | 22.1 | 2 | 105 | 12 | 2 | 25.4 | 1 | 0 | 0 | 0 | 4 | 32 | 4 | 3 | 9 | 32 | 7 | 59 | - | 21.9 | 0.46 | 58.00 | 1 | 0.00 | 5 | - | 13 | 13 | 4 | 6 | 6 | 2 | 0 | 8 | 2 | 0 | 0 | 2 | - | 0 | 1 | 42.556 | 0.88 | $ 293,648 | 94.1 | 83.8 | 11.1 | 53.0 | 2 | 193 | 11 | 20.038 | 356 | 23.69 | 18.74 | 6,544 | 33.01 | 37.0 | 2.3 | 5 | 3 | 0.0 | 0 | 97,454 | 34,186 | 1,151,575 | 5.2730 | 2.129 | 17.35 | 45,248 | 15,045 | 13 | 167,211 | 4.67 | 3 |
| 1 | Tarapacá | 0.0 | 5 | 1 | 13 | 73 | 5 | 20.8 | 2 | 178 | 12 | 1 | 12.6 | 1 | 0 | 0 | 0 | 0 | 34 | 10 | 1 | 6 | 34 | 12 | 0 | 0.2 | 9.1 | 0.03 | 76.03 | 1 | 0.00 | 16 | - | 2 | 6 | 1 | 1 | 7 | 5 | 0 | 5 | 6 | 4 | 2 | 3 | 5 | 0 | 0 | 68.563 | 1.45 | $ 381,466 | 91.7 | 66.7 | 10.7 | 42.0 | 5 | 255 | 19 | 22.180 | 380 | 23.03 | 22.17 | 11,108 | 41.43 | 42.8 | 2.1 | 5 | 10 | 0.0 | 11 | 235,365 | 40,919 | 19,560 | 4.1850 | 4.021 | 15.90 | 81,182 | 17,161 | 0 | 434,727 | 184.10 | 1 |
| 2 | Antofagasta | 1.0 | 9 | 0 | 28 | 81 | 16 | 27.4 | 8 | 203 | 15 | 2 | 5.7 | 2 | 1 | 0 | 3 | 1 | 24 | 37 | 0 | 4 | 31 | 26 | 63 | - | 2.8 | 0.03 | 39.56 | 2 | 0.00 | 22 | - | 5 | 28 | 8 | 13 | 3 | 14 | 1 | 6 | 10 | 6 | 4 | 0 | 5 | 0 | 0 | 54.486 | 1.08 | $ 475,866 | 91.7 | 66.7 | 10.6 | 40.0 | 7 | 529 | 47 | 20.446 | 184 | 24.55 | 22.76 | 19,920 | 35.02 | 44.6 | 1.8 | 2 | 10 | 17.0 | 15 | 413,922 | 84,195 | 22,898 | 2.0244 | 3.332 | 52.25 | 112,607 | 315,888 | 0 | 115,100 | 23.00 | 5 |
| 3 | Atacama | 8.0 | 10 | 0 | 8 | 35 | 7 | 20.0 | 0 | 144 | 12 | 0 | 7.5 | 0 | 0 | 0 | 0 | 0 | 18 | 5 | 0 | 2 | 29 | 7 | 33 | - | 2.0 | 0.02 | 40.76 | 0 | 3.93 | 17 | NaN | 7 | 23 | 2 | 7 | 2 | 10 | 0 | 3 | 13 | 1 | 3 | 2 | 5 | 1 | 0 | 51.844 | 0.94 | $ 379,971 | 89.1 | 73.2 | 10.3 | 47.0 | 1 | 108 | 16 | 18.479 | 578 | 19.43 | 18.49 | 9,674 | 29.25 | 36.0 | 1.9 | 1 | 3 | 0.0 | 15 | 168,508 | 14,222 | 2,416 | 0.0000 | 3.635 | 24.39 | 55,812 | 24,873 | 0 | 2,552 | 11.33 | 2 |
| 4 | Coquimbo | 23.0 | 7 | 0 | 2 | 52 | 7 | 18.3 | 4 | 80 | 22 | 2 | 1.7 | 0 | 1 | 0 | 7 | 1 | 69 | 2 | 1 | 0 | 29 | 27 | 97 | 0.8 | 0.4 | 0.35 | 25.45 | 1 | 0.00 | 37 | 1.50 | 4 | 16 | 7 | 0 | 1 | 21 | 0 | 18 | 0 | 9 | 1 | 0 | 5 | 2 | 1 | 56.498 | 0.92 | $ 338,014 | 92.7 | 71.2 | 9.7 | 43.0 | 7 | 870 | 85 | 6.963 | 1074 | 15.50 | 15.63 | 24,346 | 12.00 | 35.2 | 2.8 | 4 | 9 | 383.9 | 750 | 205,850 | 25,803 | 2,786 | 0.0000 | 1.912 | 283.37 | 116,263 | 57,234 | 0 | 15,265 | 228.58 | 1 |
| 5 | Valparaíso | 14.0 | 37 | 2 | 24 | 161 | 12 | 25.7 | 7 | 322 | 56 | 7 | 3.2 | 3 | 3 | 1 | 4 | 2 | 48 | 21 | 4 | 46 | 90 | 36 | 720 | 6.5 | 2.7 | 2.11 | 38.46 | 13 | 0.00 | 59 | 6.64 | 13 | 71 | 3 | 0 | 3 | 14 | 3 | 22 | 1 | 0 | 0 | 14 | 5 | 3 | 2 | 53.534 | 0.99 | $ 311,264 | 92.9 | 73.5 | 10.6 | 27.0 | 21 | 3,949 | 316 | 12.209 | 3661 | 17.21 | 13.39 | 44,504 | 21.18 | 29.7 | 2.0 | 14 | 27 | 920.0 | 257 | 430,436 | 106,915 | 317,309 | 0.6494 | 0.197 | 280.95 | 316,618 | 146,161 | 31 | 436,195 | 93.86 | 1 |
| 6 | Metropolitana | 4.0 | 56 | 0 | 1 | 404 | 35 | 22.5 | 70 | 6558 | 127 | 25 | 4.1 | 30 | 0 | 0 | 3 | 2 | 5 | 9 | 4 | 4 | 85 | 112 | 274 | 6.9 | 0.9 | 7.25 | 47.03 | 16 | 0.00 | 0 | 0.04 | 3 | 3 | 0 | 3 | 4 | 0 | 5 | 9 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 55.901 | 6.06 | $ 421,484 | 93.1 | 70.5 | 11.2 | 19.0 | 30 | 12,881 | 1261 | 4.834 | 15221 | 38.13 | 4.33 | 43,634 | 9.32 | 59.8 | 2.2 | 15 | 133 | 754.2 | 236 | 448,887 | 765,681 | 1,091,111 | 1.2810 | 0.731 | 541.32 | 1,306,140 | 83,459 | 0 | 1,147,039 | 248.50 | 1 |
| 7 | O'Higgins | 32.0 | 13 | 1 | 9 | 67 | 6 | 21.9 | 13 | 251 | 36 | 0 | 1.9 | 1 | 7 | 2 | 3 | 16 | 29 | 3 | 1 | 0 | 28 | 15 | 229 | 11.3 | 2.8 | 0.92 | 27.15 | 4 | 1.28 | 12 | 2.47 | 5 | 22 | 1 | 3 | 2 | 2 | 7 | 5 | 2 | 1 | 1 | 7 | 5 | 0 | 0 | 54.662 | 0.62 | $ 308,068 | 95.5 | 65.0 | 9.5 | 23.0 | 7 | 352 | 15 | 9.992 | 2833 | 17.46 | 8.01 | 14,526 | 6.34 | 15.4 | 2.2 | 2 | 7 | 10.0 | 0 | 73,478 | 7,066 | - | 0.0000 | 1.352 | 134.50 | 164,204 | 11,073 | 0 | 0 | 62.12 | 0 |
| 8 | Maule | 29.0 | 17 | 0 | 1 | 54 | 7 | 11.1 | 9 | 657 | 29 | 1 | 1.6 | 1 | 7 | 0 | 0 | 10 | 27 | 4 | 3 | 0 | 37 | 28 | 19 | 12.7 | 0.6 | 0.41 | 32.86 | 5 | 0.00 | 18 | 0.82 | 0 | 20 | 23 | 12 | 7 | 5 | 6 | 13 | 3 | 0 | 0 | 9 | 5 | 0 | 0 | 48.164 | 0.24 | $ 244,231 | 93.4 | 73.1 | 9.0 | 24.0 | 4 | 364 | 37 | 8.700 | 2898 | 14.49 | 6.10 | 12,278 | 8.67 | 28.4 | 2.0 | 3 | 14 | 0.0 | 0 | 167,293 | 8,935 | 3,942 | 0.0000 | 1.480 | 197.65 | 185,728 | 64,500 | 0 | 853 | 232.90 | 2 |
| 9 | Biobío | 5.0 | 32 | 0 | 0 | 59 | 20 | 18.9 | 12 | 488 | 63 | 7 | 3.9 | 10 | 0 | 0 | 0 | 4 | 13 | 23 | 2 | 0 | 10 | 54 | 135 | 20.7 | 2.8 | 0.96 | 31.68 | 2 | 1.07 | 25 | 0.73 | 3 | 7 | 19 | 3 | 2 | 4 | 3 | 2 | 0 | 1 | 8 | 0 | 5 | 0 | 0 | 48.651 | 1.23 | $ 290,367 | 92.7 | 71.1 | 9.9 | 23.0 | 17 | 4,023 | 320 | 1.612 | 2334 | 16.04 | 4.93 | 20,802 | 7.89 | 36.0 | 1.9 | 5 | 12 | 25.0 | 9 | 393,481 | 31,409 | 1,546 | 0.0000 | 1.021 | 400.89 | 312,085 | 49,841 | 0 | 828 | 642.26 | 1 |
| 10 | Araucanía | 18.0 | 13 | 0 | 2 | 96 | 5 | 13.2 | 8 | 179 | 58 | 4 | 30.1 | 0 | 5 | 3 | 0 | 13 | 16 | 6 | 10 | 0 | 12 | 31 | 20 | 29.4 | 9.6 | 0.43 | 36.79 | 6 | 0.00 | 25 | 0.16 | 22 | 79 | 51 | 10 | 17 | 3 | 7 | 10 | 0 | 1 | 3 | 23 | 5 | 0 | 1 | 52.282 | 2.30 | $ 251,081 | 94.5 | 72.0 | 9.1 | 29.0 | 8 | 709 | 67 | 9.085 | 709 | 11.21 | 9.90 | 26,140 | 16.09 | 37.4 | 2.5 | 15 | 7 | 24.0 | 25 | 200,377 | 38,524 | 130,713 | 0.0000 | 1.615 | 236.97 | 124,956 | 228,921 | 0 | 112,246 | 469.74 | 3 |
| 11 | Los Ríos | 3.0 | 9 | 0 | 1 | 33 | 8 | 16.2 | 3 | 94 | 12 | 2 | 16.7 | 2 | 1 | 0 | 1 | 0 | 10 | 5 | 2 | 0 | 10 | 19 | 55 | 46.1 | 6.9 | 0.31 | 35.41 | 0 | 0.00 | 10 | 0.14 | 2 | 31 | 13 | 3 | 14 | 4 | 0 | 1 | 0 | 5 | 1 | 0 | - | 0 | 1 | 47.218 | 0.41 | $ 268,648 | 93.6 | 64.8 | 9.3 | 31.0 | 4 | 444 | 58 | 8.698 | 868 | 17.28 | 18.91 | 12,846 | 11.81 | 37.9 | 1.7 | 9 | 1 | 54.1 | 0 | 113,900 | 16,070 | 11,145 | 0.0000 | 2.019 | 133.02 | 51,811 | 376 | 7 | 0 | 42.67 | 1 |
| 12 | Los Lagos | 1.0 | 24 | 1 | 0 | 64 | 11 | 19.8 | 8 | 272 | 31 | 1 | 20.8 | 2 | 0 | 0 | 0 | 0 | 38 | 3 | 3 | 0 | 57 | 30 | 94 | 56.3 | 15.9 | 0.18 | 34.77 | 1 | 0.00 | 18 | 0.00 | 12 | 41 | 17 | 10 | 9 | 10 | 3 | 6 | 0 | 31 | 0 | 0 | 7 | 3 | 1 | 54.555 | 14.97 | $ 291,431 | 92.9 | 66.7 | 9.1 | 31.0 | 10 | 856 | 72 | 20.928 | 2672 | 14.08 | 23.97 | 35,548 | 23.82 | 31.6 | 1.8 | 27 | 13 | 54.1 | 48 | 275,043 | 105,048 | 259,411 | 10.9299 | 1.893 | 135.48 | 129,882 | 486,725 | 28 | 192,977 | 660.16 | 3 |
| 13 | Aysén | 0.0 | 4 | 0 | 2 | 17 | 0 | 14.8 | 0 | 49 | 12 | 0 | 21.8 | 0 | 0 | 0 | 0 | 0 | 24 | 2 | 0 | 1 | 20 | 10 | 0 | 44.4 | 39.4 | 0.02 | 43.89 | 0 | 0.00 | 0 | 0.01 | 21 | 93 | 25 | 8 | 6 | 5 | 23 | 3 | 0 | 8 | 3 | 6 | 6 | 0 | 2 | 61.016 | 1.05 | $ 418,044 | 95.3 | 68.5 | 9.5 | 66.0 | 3 | 64 | 6 | 67.765 | 83 | 6.83 | 37.60 | 8,020 | 23.06 | 21.7 | 1.7 | 0 | 1 | 0.0 | 19 | 28,584 | 10,763 | 90,876 | 0.0000 | 4.089 | 3.70 | 19,348 | 32,030 | 0 | 56,201 | 5.00 | 4 |
| 14 | Magallanes y Antártica | 0.0 | 3 | 0 | 2 | 45 | 6 | 27.7 | 2 | 4 | 14 | 0 | 22.7 | 3 | 0 | 0 | 0 | 2 | 16 | 16 | 0 | 2 | 24 | 11 | 34 | 20.2 | 57.3 | 0.04 | 40.58 | 3 | 0.00 | 0 | 0.01 | 19 | 62 | 23 | 10 | 0 | 17 | 20 | 10 | 0 | 0 | 1 | 1 | 5 | 1 | 1 | 51.872 | 0.06 | $ 572,203 | 93.6 | 77.6 | 10.2 | 60.0 | 6 | 380 | 55 | 137.244 | 222 | 12.94 | 41.53 | 12,436 | 67.76 | 37.1 | 2.0 | 5 | 3 | 0.0 | 12 | 84,034 | 109,092 | 262,190 | 0.1650 | 3.632 | 6.02 | 46,336 | 283,629 | 18 | 388,598 | 0.00 | 5 |
In [17]:
# Types of columns
chile_data_1.dtypes
Out[17]:
Region object CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR float64 NUMBER OF CULTURAL CENTERS int64 WORLD CULTURAL HERITAGE SITES int64 NUMBER OF ARCHEOLOGICAL SITES int64 NATIONAL MONUMENTS int64 MUSEUMS int64 % OF POPULATION THAT ATTENDS MUSEUMS float64 THEATERS int64 NUMBER OF THEATER PLAYS PER YEAR int64 LIBRARIES int64 GALERIES int64 % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP float64 NUMBER OF EXHIBITS int64 ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR int64 MAJOR SPORTS EVENTS PER YEAR int64 OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS int64 ARTWORK SITES int64 POPULAR ARCHITECTURE SITES int64 HISTORICAL SITES int64 LOCAL MARKETS int64 CULTURAL SITES LEVEL III (INTERNATIONAL) int64 CULTURA SITES LEVEL II (NATIONAL) int64 CULTURAL SITES LEVEL I (LOCAL) int64 HERITAGE ARCHITECTURAL HOUSES int64 % OF LAND THAT CORRESPONDS TO FORESTS object NATIONAL PROTECTED SITES (%) float64 % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS float64 TOXIC WASTE DISPOSAL (TONS/100 hab.) float64 NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED int64 ENVIRONMENTAL ISSUES PER MILLION HABITANTS float64 NUMBER OF BEACHES AND BEACH RESORTS int64 LAND AFFECTED BY WILDFIRES object NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) int64 NATURAL PROTECTED SITES LEVEL II (NATIONAL) int64 RIVERS, LAKES AND WATERFALLS int64 MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS int64 GEISERS AND THERMAL CENTERS int64 PIERS AND SEASHORES int64 GLACIERS AND WINTER VACATION LOCATIONS int64 VALLEYS int64 DESERTS AND DUNES int64 ISLANDS AND PENINSULAS int64 PALEONTOLOGY SITES int64 HIKING TRAILS int64 PRESERVED SITES object SEASHORE PROTECTED SITES int64 BIOSHPERE RESERVES int64 % AVAILABLE WORKFORCE float64 % POPULATION ORIENTED TOWARDS TOURISM float64 AVERAGE MONTHLY INCOME (CHILEAN PESOS) object 5 POPULATION WITH PRIMARY EDUCATION float64 % POPULATION WITH SECONDARY EDUCATION float64 AVERAGE NUMBER OF YEARS STUDYING float64 HIGHER EDUCATION AND TECHNICAL INSTITUTIONS float64 TOURISM-ORIENTED INSTITUTIONS int64 NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS object AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS int64 DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) float64 CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS int64 % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR float64 ROOMS PER 1000 HABITANTS float64 NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. object TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) float64 AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR float64 AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND float64 NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION int64 NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS int64 TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS float64 TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) int64 NATIONAL TOURISTS ARRIVALS object INTERNATIONAL TOURISTS ARRIVALS object NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY object DENSITY OF AIRPORTS float64 DENSITY OF ROADS AND HIGHWAYS float64 % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) float64 NUMBER OF VEHICLES object VISITORS TO PROTECTED SITES object NUMBER OF CRUISES THAT ARRIVE PER YEAR int64 TOURIST'S ARRIVALS THROUGH BORDER LINES object SECONDARY ROADS (KMS) float64 NUMBER OF INTERNATIONAL BORDER GATES int64 dtype: object
In [18]:
# Remove $ symbol
chile_data_1 = chile_data_1.replace(r'[<$]', '', regex = True)
In [19]:
# Remove commas from numbers
chile_data_1 = chile_data_1.replace(',','', regex = True)
In [20]:
# Remove `-` character
chile_data_1 = chile_data_1.replace('-','', regex = True)
In [21]:
# Replace empty values with NaNs
chile_data_1 = chile_data_1.replace(r'^\s*$', np.nan, regex = True)
In [23]:
# Check NaNs in the dataset
chile_data_1
Out[23]:
| Region | CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR | NUMBER OF CULTURAL CENTERS | WORLD CULTURAL HERITAGE SITES | NUMBER OF ARCHEOLOGICAL SITES | NATIONAL MONUMENTS | MUSEUMS | % OF POPULATION THAT ATTENDS MUSEUMS | THEATERS | NUMBER OF THEATER PLAYS PER YEAR | LIBRARIES | GALERIES | % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP | NUMBER OF EXHIBITS | ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR | MAJOR SPORTS EVENTS PER YEAR | OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS | ARTWORK SITES | POPULAR ARCHITECTURE SITES | HISTORICAL SITES | LOCAL MARKETS | CULTURAL SITES LEVEL III (INTERNATIONAL) | CULTURA SITES LEVEL II (NATIONAL) | CULTURAL SITES LEVEL I (LOCAL) | HERITAGE ARCHITECTURAL HOUSES | % OF LAND THAT CORRESPONDS TO FORESTS | NATIONAL PROTECTED SITES (%) | % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS | TOXIC WASTE DISPOSAL (TONS/100 hab.) | NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED | ENVIRONMENTAL ISSUES PER MILLION HABITANTS | NUMBER OF BEACHES AND BEACH RESORTS | LAND AFFECTED BY WILDFIRES | NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) | NATURAL PROTECTED SITES LEVEL II (NATIONAL) | RIVERS, LAKES AND WATERFALLS | MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS | GEISERS AND THERMAL CENTERS | PIERS AND SEASHORES | GLACIERS AND WINTER VACATION LOCATIONS | VALLEYS | DESERTS AND DUNES | ISLANDS AND PENINSULAS | PALEONTOLOGY SITES | HIKING TRAILS | PRESERVED SITES | SEASHORE PROTECTED SITES | BIOSHPERE RESERVES | % AVAILABLE WORKFORCE | % POPULATION ORIENTED TOWARDS TOURISM | AVERAGE MONTHLY INCOME (CHILEAN PESOS) | 5 POPULATION WITH PRIMARY EDUCATION | % POPULATION WITH SECONDARY EDUCATION | AVERAGE NUMBER OF YEARS STUDYING | HIGHER EDUCATION AND TECHNICAL INSTITUTIONS | TOURISM-ORIENTED INSTITUTIONS | NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS | AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS | DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) | CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS | % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR | ROOMS PER 1000 HABITANTS | NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. | TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) | AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR | AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND | NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION | NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS | TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS | TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) | NATIONAL TOURISTS ARRIVALS | INTERNATIONAL TOURISTS ARRIVALS | NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY | DENSITY OF AIRPORTS | DENSITY OF ROADS AND HIGHWAYS | % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) | NUMBER OF VEHICLES | VISITORS TO PROTECTED SITES | NUMBER OF CRUISES THAT ARRIVE PER YEAR | TOURIST'S ARRIVALS THROUGH BORDER LINES | SECONDARY ROADS (KMS) | NUMBER OF INTERNATIONAL BORDER GATES | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Arica y Parinacota | 31.0 | 2 | 0 | 30 | 28 | 4 | 22.1 | 2 | 105 | 12 | 2 | 25.4 | 1 | 0 | 0 | 0 | 4 | 32 | 4 | 3 | 9 | 32 | 7 | 59 | NaN | 21.9 | 0.46 | 58.00 | 1 | 0.00 | 5 | NaN | 13 | 13 | 4 | 6 | 6 | 2 | 0 | 8 | 2 | 0 | 0 | 2 | NaN | 0 | 1 | 42.556 | 0.88 | 293648 | 94.1 | 83.8 | 11.1 | 53.0 | 2 | 193 | 11 | 20.038 | 356 | 23.69 | 18.74 | 6544 | 33.01 | 37.0 | 2.3 | 5 | 3 | 0.0 | 0 | 97454 | 34186 | 1151575 | 5.2730 | 2.129 | 17.35 | 45248 | 15045 | 13 | 167211 | 4.67 | 3 |
| 1 | Tarapacá | 0.0 | 5 | 1 | 13 | 73 | 5 | 20.8 | 2 | 178 | 12 | 1 | 12.6 | 1 | 0 | 0 | 0 | 0 | 34 | 10 | 1 | 6 | 34 | 12 | 0 | 0.2 | 9.1 | 0.03 | 76.03 | 1 | 0.00 | 16 | NaN | 2 | 6 | 1 | 1 | 7 | 5 | 0 | 5 | 6 | 4 | 2 | 3 | 5 | 0 | 0 | 68.563 | 1.45 | 381466 | 91.7 | 66.7 | 10.7 | 42.0 | 5 | 255 | 19 | 22.180 | 380 | 23.03 | 22.17 | 11108 | 41.43 | 42.8 | 2.1 | 5 | 10 | 0.0 | 11 | 235365 | 40919 | 19560 | 4.1850 | 4.021 | 15.90 | 81182 | 17161 | 0 | 434727 | 184.10 | 1 |
| 2 | Antofagasta | 1.0 | 9 | 0 | 28 | 81 | 16 | 27.4 | 8 | 203 | 15 | 2 | 5.7 | 2 | 1 | 0 | 3 | 1 | 24 | 37 | 0 | 4 | 31 | 26 | 63 | NaN | 2.8 | 0.03 | 39.56 | 2 | 0.00 | 22 | NaN | 5 | 28 | 8 | 13 | 3 | 14 | 1 | 6 | 10 | 6 | 4 | 0 | 5 | 0 | 0 | 54.486 | 1.08 | 475866 | 91.7 | 66.7 | 10.6 | 40.0 | 7 | 529 | 47 | 20.446 | 184 | 24.55 | 22.76 | 19920 | 35.02 | 44.6 | 1.8 | 2 | 10 | 17.0 | 15 | 413922 | 84195 | 22898 | 2.0244 | 3.332 | 52.25 | 112607 | 315888 | 0 | 115100 | 23.00 | 5 |
| 3 | Atacama | 8.0 | 10 | 0 | 8 | 35 | 7 | 20.0 | 0 | 144 | 12 | 0 | 7.5 | 0 | 0 | 0 | 0 | 0 | 18 | 5 | 0 | 2 | 29 | 7 | 33 | NaN | 2.0 | 0.02 | 40.76 | 0 | 3.93 | 17 | NaN | 7 | 23 | 2 | 7 | 2 | 10 | 0 | 3 | 13 | 1 | 3 | 2 | 5 | 1 | 0 | 51.844 | 0.94 | 379971 | 89.1 | 73.2 | 10.3 | 47.0 | 1 | 108 | 16 | 18.479 | 578 | 19.43 | 18.49 | 9674 | 29.25 | 36.0 | 1.9 | 1 | 3 | 0.0 | 15 | 168508 | 14222 | 2416 | 0.0000 | 3.635 | 24.39 | 55812 | 24873 | 0 | 2552 | 11.33 | 2 |
| 4 | Coquimbo | 23.0 | 7 | 0 | 2 | 52 | 7 | 18.3 | 4 | 80 | 22 | 2 | 1.7 | 0 | 1 | 0 | 7 | 1 | 69 | 2 | 1 | 0 | 29 | 27 | 97 | 0.8 | 0.4 | 0.35 | 25.45 | 1 | 0.00 | 37 | 1.50 | 4 | 16 | 7 | 0 | 1 | 21 | 0 | 18 | 0 | 9 | 1 | 0 | 5 | 2 | 1 | 56.498 | 0.92 | 338014 | 92.7 | 71.2 | 9.7 | 43.0 | 7 | 870 | 85 | 6.963 | 1074 | 15.50 | 15.63 | 24346 | 12.00 | 35.2 | 2.8 | 4 | 9 | 383.9 | 750 | 205850 | 25803 | 2786 | 0.0000 | 1.912 | 283.37 | 116263 | 57234 | 0 | 15265 | 228.58 | 1 |
| 5 | Valparaíso | 14.0 | 37 | 2 | 24 | 161 | 12 | 25.7 | 7 | 322 | 56 | 7 | 3.2 | 3 | 3 | 1 | 4 | 2 | 48 | 21 | 4 | 46 | 90 | 36 | 720 | 6.5 | 2.7 | 2.11 | 38.46 | 13 | 0.00 | 59 | 6.64 | 13 | 71 | 3 | 0 | 3 | 14 | 3 | 22 | 1 | 0 | 0 | 14 | 5 | 3 | 2 | 53.534 | 0.99 | 311264 | 92.9 | 73.5 | 10.6 | 27.0 | 21 | 3949 | 316 | 12.209 | 3661 | 17.21 | 13.39 | 44504 | 21.18 | 29.7 | 2.0 | 14 | 27 | 920.0 | 257 | 430436 | 106915 | 317309 | 0.6494 | 0.197 | 280.95 | 316618 | 146161 | 31 | 436195 | 93.86 | 1 |
| 6 | Metropolitana | 4.0 | 56 | 0 | 1 | 404 | 35 | 22.5 | 70 | 6558 | 127 | 25 | 4.1 | 30 | 0 | 0 | 3 | 2 | 5 | 9 | 4 | 4 | 85 | 112 | 274 | 6.9 | 0.9 | 7.25 | 47.03 | 16 | 0.00 | 0 | 0.04 | 3 | 3 | 0 | 3 | 4 | 0 | 5 | 9 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 55.901 | 6.06 | 421484 | 93.1 | 70.5 | 11.2 | 19.0 | 30 | 12881 | 1261 | 4.834 | 15221 | 38.13 | 4.33 | 43634 | 9.32 | 59.8 | 2.2 | 15 | 133 | 754.2 | 236 | 448887 | 765681 | 1091111 | 1.2810 | 0.731 | 541.32 | 1306140 | 83459 | 0 | 1147039 | 248.50 | 1 |
| 7 | O'Higgins | 32.0 | 13 | 1 | 9 | 67 | 6 | 21.9 | 13 | 251 | 36 | 0 | 1.9 | 1 | 7 | 2 | 3 | 16 | 29 | 3 | 1 | 0 | 28 | 15 | 229 | 11.3 | 2.8 | 0.92 | 27.15 | 4 | 1.28 | 12 | 2.47 | 5 | 22 | 1 | 3 | 2 | 2 | 7 | 5 | 2 | 1 | 1 | 7 | 5 | 0 | 0 | 54.662 | 0.62 | 308068 | 95.5 | 65.0 | 9.5 | 23.0 | 7 | 352 | 15 | 9.992 | 2833 | 17.46 | 8.01 | 14526 | 6.34 | 15.4 | 2.2 | 2 | 7 | 10.0 | 0 | 73478 | 7066 | NaN | 0.0000 | 1.352 | 134.50 | 164204 | 11073 | 0 | 0 | 62.12 | 0 |
| 8 | Maule | 29.0 | 17 | 0 | 1 | 54 | 7 | 11.1 | 9 | 657 | 29 | 1 | 1.6 | 1 | 7 | 0 | 0 | 10 | 27 | 4 | 3 | 0 | 37 | 28 | 19 | 12.7 | 0.6 | 0.41 | 32.86 | 5 | 0.00 | 18 | 0.82 | 0 | 20 | 23 | 12 | 7 | 5 | 6 | 13 | 3 | 0 | 0 | 9 | 5 | 0 | 0 | 48.164 | 0.24 | 244231 | 93.4 | 73.1 | 9.0 | 24.0 | 4 | 364 | 37 | 8.700 | 2898 | 14.49 | 6.10 | 12278 | 8.67 | 28.4 | 2.0 | 3 | 14 | 0.0 | 0 | 167293 | 8935 | 3942 | 0.0000 | 1.480 | 197.65 | 185728 | 64500 | 0 | 853 | 232.90 | 2 |
| 9 | Biobío | 5.0 | 32 | 0 | 0 | 59 | 20 | 18.9 | 12 | 488 | 63 | 7 | 3.9 | 10 | 0 | 0 | 0 | 4 | 13 | 23 | 2 | 0 | 10 | 54 | 135 | 20.7 | 2.8 | 0.96 | 31.68 | 2 | 1.07 | 25 | 0.73 | 3 | 7 | 19 | 3 | 2 | 4 | 3 | 2 | 0 | 1 | 8 | 0 | 5 | 0 | 0 | 48.651 | 1.23 | 290367 | 92.7 | 71.1 | 9.9 | 23.0 | 17 | 4023 | 320 | 1.612 | 2334 | 16.04 | 4.93 | 20802 | 7.89 | 36.0 | 1.9 | 5 | 12 | 25.0 | 9 | 393481 | 31409 | 1546 | 0.0000 | 1.021 | 400.89 | 312085 | 49841 | 0 | 828 | 642.26 | 1 |
| 10 | Araucanía | 18.0 | 13 | 0 | 2 | 96 | 5 | 13.2 | 8 | 179 | 58 | 4 | 30.1 | 0 | 5 | 3 | 0 | 13 | 16 | 6 | 10 | 0 | 12 | 31 | 20 | 29.4 | 9.6 | 0.43 | 36.79 | 6 | 0.00 | 25 | 0.16 | 22 | 79 | 51 | 10 | 17 | 3 | 7 | 10 | 0 | 1 | 3 | 23 | 5 | 0 | 1 | 52.282 | 2.30 | 251081 | 94.5 | 72.0 | 9.1 | 29.0 | 8 | 709 | 67 | 9.085 | 709 | 11.21 | 9.90 | 26140 | 16.09 | 37.4 | 2.5 | 15 | 7 | 24.0 | 25 | 200377 | 38524 | 130713 | 0.0000 | 1.615 | 236.97 | 124956 | 228921 | 0 | 112246 | 469.74 | 3 |
| 11 | Los Ríos | 3.0 | 9 | 0 | 1 | 33 | 8 | 16.2 | 3 | 94 | 12 | 2 | 16.7 | 2 | 1 | 0 | 1 | 0 | 10 | 5 | 2 | 0 | 10 | 19 | 55 | 46.1 | 6.9 | 0.31 | 35.41 | 0 | 0.00 | 10 | 0.14 | 2 | 31 | 13 | 3 | 14 | 4 | 0 | 1 | 0 | 5 | 1 | 0 | NaN | 0 | 1 | 47.218 | 0.41 | 268648 | 93.6 | 64.8 | 9.3 | 31.0 | 4 | 444 | 58 | 8.698 | 868 | 17.28 | 18.91 | 12846 | 11.81 | 37.9 | 1.7 | 9 | 1 | 54.1 | 0 | 113900 | 16070 | 11145 | 0.0000 | 2.019 | 133.02 | 51811 | 376 | 7 | 0 | 42.67 | 1 |
| 12 | Los Lagos | 1.0 | 24 | 1 | 0 | 64 | 11 | 19.8 | 8 | 272 | 31 | 1 | 20.8 | 2 | 0 | 0 | 0 | 0 | 38 | 3 | 3 | 0 | 57 | 30 | 94 | 56.3 | 15.9 | 0.18 | 34.77 | 1 | 0.00 | 18 | 0.00 | 12 | 41 | 17 | 10 | 9 | 10 | 3 | 6 | 0 | 31 | 0 | 0 | 7 | 3 | 1 | 54.555 | 14.97 | 291431 | 92.9 | 66.7 | 9.1 | 31.0 | 10 | 856 | 72 | 20.928 | 2672 | 14.08 | 23.97 | 35548 | 23.82 | 31.6 | 1.8 | 27 | 13 | 54.1 | 48 | 275043 | 105048 | 259411 | 10.9299 | 1.893 | 135.48 | 129882 | 486725 | 28 | 192977 | 660.16 | 3 |
| 13 | Aysén | 0.0 | 4 | 0 | 2 | 17 | 0 | 14.8 | 0 | 49 | 12 | 0 | 21.8 | 0 | 0 | 0 | 0 | 0 | 24 | 2 | 0 | 1 | 20 | 10 | 0 | 44.4 | 39.4 | 0.02 | 43.89 | 0 | 0.00 | 0 | 0.01 | 21 | 93 | 25 | 8 | 6 | 5 | 23 | 3 | 0 | 8 | 3 | 6 | 6 | 0 | 2 | 61.016 | 1.05 | 418044 | 95.3 | 68.5 | 9.5 | 66.0 | 3 | 64 | 6 | 67.765 | 83 | 6.83 | 37.60 | 8020 | 23.06 | 21.7 | 1.7 | 0 | 1 | 0.0 | 19 | 28584 | 10763 | 90876 | 0.0000 | 4.089 | 3.70 | 19348 | 32030 | 0 | 56201 | 5.00 | 4 |
| 14 | Magallanes y Antártica | 0.0 | 3 | 0 | 2 | 45 | 6 | 27.7 | 2 | 4 | 14 | 0 | 22.7 | 3 | 0 | 0 | 0 | 2 | 16 | 16 | 0 | 2 | 24 | 11 | 34 | 20.2 | 57.3 | 0.04 | 40.58 | 3 | 0.00 | 0 | 0.01 | 19 | 62 | 23 | 10 | 0 | 17 | 20 | 10 | 0 | 0 | 1 | 1 | 5 | 1 | 1 | 51.872 | 0.06 | 572203 | 93.6 | 77.6 | 10.2 | 60.0 | 6 | 380 | 55 | 137.244 | 222 | 12.94 | 41.53 | 12436 | 67.76 | 37.1 | 2.0 | 5 | 3 | 0.0 | 12 | 84034 | 109092 | 262190 | 0.1650 | 3.632 | 6.02 | 46336 | 283629 | 18 | 388598 | 0.00 | 5 |
Now after replacement we have NaNs in 4 columns:
- % OF LAND THAT CORRESPONDS TO FORESTS (3 NaNs)
- LAND AFFECTED BY WILDFIRES (4 NaNs)
- PRESERVED SITES (2 NaNs)
- NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY (1 NaN)
In [24]:
# Impute data in four columns
imputer = SimpleImputer(missing_values = np.nan, strategy = 'mean')
chile_data_1[['% OF LAND THAT CORRESPONDS TO FORESTS',
'LAND AFFECTED BY WILDFIRES',
'PRESERVED SITES',
'NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY']] = imputer.fit_transform(chile_data_1[['% OF LAND THAT CORRESPONDS TO FORESTS',
'LAND AFFECTED BY WILDFIRES',
'PRESERVED SITES',
'NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY']])
In [25]:
# Check NaNs in the dataset
chile_data_1.isnull().sum(axis = 0)
# As we can see below, we do not have any missing values anymore
Out[25]:
Region 0 CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR 0 NUMBER OF CULTURAL CENTERS 0 WORLD CULTURAL HERITAGE SITES 0 NUMBER OF ARCHEOLOGICAL SITES 0 NATIONAL MONUMENTS 0 MUSEUMS 0 % OF POPULATION THAT ATTENDS MUSEUMS 0 THEATERS 0 NUMBER OF THEATER PLAYS PER YEAR 0 LIBRARIES 0 GALERIES 0 % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP 0 NUMBER OF EXHIBITS 0 ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR 0 MAJOR SPORTS EVENTS PER YEAR 0 OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS 0 ARTWORK SITES 0 POPULAR ARCHITECTURE SITES 0 HISTORICAL SITES 0 LOCAL MARKETS 0 CULTURAL SITES LEVEL III (INTERNATIONAL) 0 CULTURA SITES LEVEL II (NATIONAL) 0 CULTURAL SITES LEVEL I (LOCAL) 0 HERITAGE ARCHITECTURAL HOUSES 0 % OF LAND THAT CORRESPONDS TO FORESTS 0 NATIONAL PROTECTED SITES (%) 0 % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS 0 TOXIC WASTE DISPOSAL (TONS/100 hab.) 0 NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED 0 ENVIRONMENTAL ISSUES PER MILLION HABITANTS 0 NUMBER OF BEACHES AND BEACH RESORTS 0 LAND AFFECTED BY WILDFIRES 0 NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) 0 NATURAL PROTECTED SITES LEVEL II (NATIONAL) 0 RIVERS, LAKES AND WATERFALLS 0 MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS 0 GEISERS AND THERMAL CENTERS 0 PIERS AND SEASHORES 0 GLACIERS AND WINTER VACATION LOCATIONS 0 VALLEYS 0 DESERTS AND DUNES 0 ISLANDS AND PENINSULAS 0 PALEONTOLOGY SITES 0 HIKING TRAILS 0 PRESERVED SITES 0 SEASHORE PROTECTED SITES 0 BIOSHPERE RESERVES 0 % AVAILABLE WORKFORCE 0 % POPULATION ORIENTED TOWARDS TOURISM 0 AVERAGE MONTHLY INCOME (CHILEAN PESOS) 0 5 POPULATION WITH PRIMARY EDUCATION 0 % POPULATION WITH SECONDARY EDUCATION 0 AVERAGE NUMBER OF YEARS STUDYING 0 HIGHER EDUCATION AND TECHNICAL INSTITUTIONS 0 TOURISM-ORIENTED INSTITUTIONS 0 NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS 0 AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS 0 DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) 0 CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS 0 % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR 0 ROOMS PER 1000 HABITANTS 0 NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. 0 TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) 0 AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR 0 AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND 0 NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION 0 NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS 0 TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS 0 TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) 0 NATIONAL TOURISTS ARRIVALS 0 INTERNATIONAL TOURISTS ARRIVALS 0 NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY 0 DENSITY OF AIRPORTS 0 DENSITY OF ROADS AND HIGHWAYS 0 % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) 0 NUMBER OF VEHICLES 0 VISITORS TO PROTECTED SITES 0 NUMBER OF CRUISES THAT ARRIVE PER YEAR 0 TOURIST'S ARRIVALS THROUGH BORDER LINES 0 SECONDARY ROADS (KMS) 0 NUMBER OF INTERNATIONAL BORDER GATES 0 dtype: int64
In [26]:
# Let's check the correlation
cor = chile_data_1.corr()
cor.loc[:,:] = np.tril(cor, k=-1)
cor = cor.stack()
cor[(cor > 0.55) | (cor < -0.55)]
# We can see a lot of correlated variables here
Out[26]:
NATIONAL MONUMENTS NUMBER OF CULTURAL CENTERS 0.844259
MUSEUMS NUMBER OF CULTURAL CENTERS 0.863417
NATIONAL MONUMENTS 0.842485
% OF POPULATION THAT ATTENDS MUSEUMS NUMBER OF ARCHEOLOGICAL SITES 0.563521
THEATERS NUMBER OF CULTURAL CENTERS 0.810156
NATIONAL MONUMENTS 0.943688
MUSEUMS 0.872746
NUMBER OF THEATER PLAYS PER YEAR NUMBER OF CULTURAL CENTERS 0.771981
NATIONAL MONUMENTS 0.938400
MUSEUMS 0.839970
THEATERS 0.982734
LIBRARIES NUMBER OF CULTURAL CENTERS 0.916630
NATIONAL MONUMENTS 0.897171
MUSEUMS 0.829503
THEATERS 0.893389
NUMBER OF THEATER PLAYS PER YEAR 0.844666
GALERIES NUMBER OF CULTURAL CENTERS 0.865912
NATIONAL MONUMENTS 0.953502
MUSEUMS 0.893275
THEATERS 0.942789
NUMBER OF THEATER PLAYS PER YEAR 0.942964
LIBRARIES 0.926493
NUMBER OF EXHIBITS NUMBER OF CULTURAL CENTERS 0.822328
NATIONAL MONUMENTS 0.899347
MUSEUMS 0.917726
THEATERS 0.954800
NUMBER OF THEATER PLAYS PER YEAR 0.955996
LIBRARIES 0.869963
GALERIES 0.958262
ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR 0.689568
MAJOR SPORTS EVENTS PER YEAR ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR 0.668614
OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP -0.586896
ARTWORK SITES CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR 0.706580
ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR 0.879726
MAJOR SPORTS EVENTS PER YEAR 0.779169
POPULAR ARCHITECTURE SITES OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS 0.606588
HISTORICAL SITES % OF POPULATION THAT ATTENDS MUSEUMS 0.614430
LOCAL MARKETS MAJOR SPORTS EVENTS PER YEAR 0.682234
CULTURAL SITES LEVEL III (INTERNATIONAL) WORLD CULTURAL HERITAGE SITES 0.726786
NUMBER OF ARCHEOLOGICAL SITES 0.565346
CULTURA SITES LEVEL II (NATIONAL) NUMBER OF CULTURAL CENTERS 0.714245
WORLD CULTURAL HERITAGE SITES 0.585161
NATIONAL MONUMENTS 0.730111
THEATERS 0.567716
NUMBER OF THEATER PLAYS PER YEAR 0.583450
LIBRARIES 0.555219
GALERIES 0.603571
CULTURAL SITES LEVEL III (INTERNATIONAL) 0.658129
CULTURAL SITES LEVEL I (LOCAL) NUMBER OF CULTURAL CENTERS 0.908111
NATIONAL MONUMENTS 0.904602
MUSEUMS 0.932003
THEATERS 0.931128
NUMBER OF THEATER PLAYS PER YEAR 0.901929
LIBRARIES 0.944968
GALERIES 0.957579
NUMBER OF EXHIBITS 0.937123
HERITAGE ARCHITECTURAL HOUSES NUMBER OF CULTURAL CENTERS 0.640105
WORLD CULTURAL HERITAGE SITES 0.726194
CULTURAL SITES LEVEL III (INTERNATIONAL) 0.869237
CULTURA SITES LEVEL II (NATIONAL) 0.745822
% OF LAND THAT CORRESPONDS TO FORESTS % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP 0.608867
NATIONAL PROTECTED SITES (%) % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP 0.664562
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS NUMBER OF CULTURAL CENTERS 0.852563
NATIONAL MONUMENTS 0.968412
MUSEUMS 0.839991
THEATERS 0.961513
NUMBER OF THEATER PLAYS PER YEAR 0.961879
LIBRARIES 0.902624
GALERIES 0.970843
NUMBER OF EXHIBITS 0.940131
CULTURA SITES LEVEL II (NATIONAL) 0.682977
CULTURAL SITES LEVEL I (LOCAL) 0.910700
NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED NUMBER OF CULTURAL CENTERS 0.822546
NATIONAL MONUMENTS 0.888001
MUSEUMS 0.657818
THEATERS 0.761523
NUMBER OF THEATER PLAYS PER YEAR 0.736817
LIBRARIES 0.850517
GALERIES 0.813042
NUMBER OF EXHIBITS 0.698575
CULTURA SITES LEVEL II (NATIONAL) 0.777774
CULTURAL SITES LEVEL I (LOCAL) 0.763989
HERITAGE ARCHITECTURAL HOUSES 0.723040
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS 0.852965
NUMBER OF BEACHES AND BEACH RESORTS WORLD CULTURAL HERITAGE SITES 0.564417
POPULAR ARCHITECTURE SITES 0.611618
CULTURAL SITES LEVEL III (INTERNATIONAL) 0.657358
HERITAGE ARCHITECTURAL HOUSES 0.630881
LAND AFFECTED BY WILDFIRES WORLD CULTURAL HERITAGE SITES 0.772021
CULTURAL SITES LEVEL III (INTERNATIONAL) 0.893984
HERITAGE ARCHITECTURAL HOUSES 0.861942
NUMBER OF BEACHES AND BEACH RESORTS 0.773169
NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP 0.757051
NATIONAL PROTECTED SITES (%) 0.702737
NATURAL PROTECTED SITES LEVEL II (NATIONAL) NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) 0.856296
RIVERS, LAKES AND WATERFALLS % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP 0.581572
LOCAL MARKETS 0.564126
NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) 0.582272
NATURAL PROTECTED SITES LEVEL II (NATIONAL) 0.613234
GEISERS AND THERMAL CENTERS % OF POPULATION THAT ATTENDS MUSEUMS -0.606273
% OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP 0.582967
LOCAL MARKETS 0.679535
RIVERS, LAKES AND WATERFALLS 0.579717
PIERS AND SEASHORES POPULAR ARCHITECTURE SITES 0.601955
GLACIERS AND WINTER VACATION LOCATIONS NATIONAL PROTECTED SITES (%) 0.795207
NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) 0.644091
NATURAL PROTECTED SITES LEVEL II (NATIONAL) 0.675970
VALLEYS OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS 0.597619
POPULAR ARCHITECTURE SITES 0.642939
CULTURAL SITES LEVEL III (INTERNATIONAL) 0.626057
CULTURA SITES LEVEL II (NATIONAL) 0.555263
HERITAGE ARCHITECTURAL HOUSES 0.594042
NUMBER OF BEACHES AND BEACH RESORTS 0.657487
LAND AFFECTED BY WILDFIRES 0.625926
DESERTS AND DUNES ENVIRONMENTAL ISSUES PER MILLION HABITANTS 0.634685
ISLANDS AND PENINSULAS % OF LAND THAT CORRESPONDS TO FORESTS 0.602973
HIKING TRAILS ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR 0.659527
MAJOR SPORTS EVENTS PER YEAR 0.834715
ARTWORK SITES 0.618133
LOCAL MARKETS 0.744123
NATURAL PROTECTED SITES LEVEL II (NATIONAL) 0.588355
RIVERS, LAKES AND WATERFALLS 0.578786
PRESERVED SITES % OF LAND THAT CORRESPONDS TO FORESTS 0.761995
ISLANDS AND PENINSULAS 0.900599
SEASHORE PROTECTED SITES WORLD CULTURAL HERITAGE SITES 0.589369
POPULAR ARCHITECTURE SITES 0.635884
NUMBER OF BEACHES AND BEACH RESORTS 0.601448
PIERS AND SEASHORES 0.652696
VALLEYS 0.567317
BIOSHPERE RESERVES NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) 0.703191
NATURAL PROTECTED SITES LEVEL II (NATIONAL) 0.782184
% POPULATION ORIENTED TOWARDS TOURISM ISLANDS AND PENINSULAS 0.829687
PRESERVED SITES 0.799716
5 POPULATION WITH PRIMARY EDUCATION ENVIRONMENTAL ISSUES PER MILLION HABITANTS -0.569371
DESERTS AND DUNES -0.756879
AVERAGE NUMBER OF YEARS STUDYING NUMBER OF ARCHEOLOGICAL SITES 0.627887
% OF POPULATION THAT ATTENDS MUSEUMS 0.699837
TOXIC WASTE DISPOSAL (TONS/100 hab.) 0.611759
RIVERS, LAKES AND WATERFALLS -0.634409
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS NUMBER OF CULTURAL CENTERS -0.706994
MUSEUMS -0.582289
LIBRARIES -0.661580
CULTURAL SITES LEVEL I (LOCAL) -0.618349
NATIONAL PROTECTED SITES (%) 0.755444
TOURISM-ORIENTED INSTITUTIONS NUMBER OF CULTURAL CENTERS 0.946453
NATIONAL MONUMENTS 0.877200
MUSEUMS 0.864319
THEATERS 0.807503
NUMBER OF THEATER PLAYS PER YEAR 0.762069
LIBRARIES 0.918401
GALERIES 0.893880
NUMBER OF EXHIBITS 0.839887
CULTURA SITES LEVEL II (NATIONAL) 0.701367
CULTURAL SITES LEVEL I (LOCAL) 0.909699
HERITAGE ARCHITECTURAL HOUSES 0.683748
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS 0.867091
NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED 0.852548
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS -0.595463
AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS NUMBER OF CULTURAL CENTERS 0.875130
NATIONAL MONUMENTS 0.953913
MUSEUMS 0.902225
THEATERS 0.954728
NUMBER OF THEATER PLAYS PER YEAR 0.959102
LIBRARIES 0.912114
GALERIES 0.989141
NUMBER OF EXHIBITS 0.976702
CULTURA SITES LEVEL II (NATIONAL) 0.631276
CULTURAL SITES LEVEL I (LOCAL) 0.954548
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS 0.979264
NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED 0.802655
TOURISM-ORIENTED INSTITUTIONS 0.897154
DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) NATIONAL PROTECTED SITES (%) 0.937671
NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) 0.573443
GLACIERS AND WINTER VACATION LOCATIONS 0.782452
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS 0.714893
CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS NUMBER OF CULTURAL CENTERS 0.873165
NATIONAL MONUMENTS 0.946348
MUSEUMS 0.846557
THEATERS 0.974387
NUMBER OF THEATER PLAYS PER YEAR 0.967363
LIBRARIES 0.894293
GALERIES 0.934461
NUMBER OF EXHIBITS 0.931883
CULTURA SITES LEVEL II (NATIONAL) 0.690370
CULTURAL SITES LEVEL I (LOCAL) 0.919552
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS 0.976382
NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED 0.814796
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS -0.558648
TOURISM-ORIENTED INSTITUTIONS 0.841684
AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS 0.957844
% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR NATIONAL MONUMENTS 0.737713
MUSEUMS 0.738595
THEATERS 0.737251
NUMBER OF THEATER PLAYS PER YEAR 0.756636
GALERIES 0.719589
NUMBER OF EXHIBITS 0.728251
CULTURAL SITES LEVEL I (LOCAL) 0.616409
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS 0.727710
NATURAL PROTECTED SITES LEVEL II (NATIONAL) -0.667438
RIVERS, LAKES AND WATERFALLS -0.638534
AVERAGE NUMBER OF YEARS STUDYING 0.757377
AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS 0.709608
CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS 0.696218
ROOMS PER 1000 HABITANTS NUMBER OF CULTURAL CENTERS -0.588783
LIBRARIES -0.621327
CULTURAL SITES LEVEL I (LOCAL) -0.554075
NATIONAL PROTECTED SITES (%) 0.848860
NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) 0.551912
GLACIERS AND WINTER VACATION LOCATIONS 0.584152
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS 0.870295
DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) 0.845965
TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) % OF POPULATION THAT ATTENDS MUSEUMS 0.572066
NATIONAL PROTECTED SITES (%) 0.718213
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS 0.697431
DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) 0.800771
ROOMS PER 1000 HABITANTS 0.785480
AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR NATIONAL MONUMENTS 0.632719
MUSEUMS 0.683133
THEATERS 0.594317
NUMBER OF THEATER PLAYS PER YEAR 0.648942
GALERIES 0.674487
NUMBER OF EXHIBITS 0.666660
CULTURAL SITES LEVEL I (LOCAL) 0.610419
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS 0.561508
AVERAGE NUMBER OF YEARS STUDYING 0.595236
AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS 0.643566
% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR 0.757038
AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR 0.599157
% OF LAND THAT CORRESPONDS TO FORESTS -0.571184
NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION LOCAL MARKETS 0.589931
ISLANDS AND PENINSULAS 0.577918
PRESERVED SITES 0.550789
% POPULATION ORIENTED TOWARDS TOURISM 0.834428
NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS NUMBER OF CULTURAL CENTERS 0.826176
NATIONAL MONUMENTS 0.975323
MUSEUMS 0.858148
THEATERS 0.975050
NUMBER OF THEATER PLAYS PER YEAR 0.986928
LIBRARIES 0.871143
GALERIES 0.960217
NUMBER OF EXHIBITS 0.946637
CULTURA SITES LEVEL II (NATIONAL) 0.687789
CULTURAL SITES LEVEL I (LOCAL) 0.919301
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS 0.979435
NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED 0.808640
TOURISM-ORIENTED INSTITUTIONS 0.832542
AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS 0.974671
CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS 0.977689
% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR 0.758104
AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR 0.644461
TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS NUMBER OF CULTURAL CENTERS 0.730785
NATIONAL MONUMENTS 0.745783
MUSEUMS 0.560161
THEATERS 0.561341
NUMBER OF THEATER PLAYS PER YEAR 0.567172
LIBRARIES 0.650548
GALERIES 0.699662
OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS 0.634816
CULTURAL SITES LEVEL III (INTERNATIONAL) 0.707349
CULTURA SITES LEVEL II (NATIONAL) 0.831304
CULTURAL SITES LEVEL I (LOCAL) 0.633719
HERITAGE ARCHITECTURAL HOUSES 0.848293
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS 0.730927
NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED 0.828897
LAND AFFECTED BY WILDFIRES 0.607023
VALLEYS 0.677079
TOURISM-ORIENTED INSTITUTIONS 0.799911
AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS 0.701268
CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS 0.666246
NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS 0.676287
TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS 0.851766
POPULAR ARCHITECTURE SITES 0.696802
PIERS AND SEASHORES 0.562667
VALLEYS 0.660357
AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND 0.654014
TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS 0.598853
NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY NATIONAL MONUMENTS 0.565476
THEATERS 0.582208
NUMBER OF THEATER PLAYS PER YEAR 0.606793
GALERIES 0.591355
NUMBER OF EXHIBITS 0.571064
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS 0.641856
AVERAGE NUMBER OF YEARS STUDYING 0.607522
AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS 0.572282
CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS 0.582579
% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR 0.618334
NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS 0.601784
DENSITY OF AIRPORTS ISLANDS AND PENINSULAS 0.757488
PRESERVED SITES 0.733560
% POPULATION ORIENTED TOWARDS TOURISM 0.802385
NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION 0.613059
DENSITY OF ROADS AND HIGHWAYS NUMBER OF CULTURAL CENTERS -0.725310
LIBRARIES -0.693484
LOCAL MARKETS -0.562029
CULTURAL SITES LEVEL I (LOCAL) -0.603210
HERITAGE ARCHITECTURAL HOUSES -0.667809
NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED -0.658522
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS 0.810277
TOURISM-ORIENTED INSTITUTIONS -0.674614
DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) 0.574429
CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS -0.556551
ROOMS PER 1000 HABITANTS 0.778561
TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) 0.676773
TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS -0.605450
% OF ROADS THAT ARE HIGHWAYS (FOUR LINES) NUMBER OF CULTURAL CENTERS 0.862005
NATIONAL MONUMENTS 0.732969
MUSEUMS 0.781299
THEATERS 0.749427
NUMBER OF THEATER PLAYS PER YEAR 0.690804
LIBRARIES 0.898907
GALERIES 0.819953
NUMBER OF EXHIBITS 0.747979
CULTURAL SITES LEVEL I (LOCAL) 0.900965
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS 0.764096
NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED 0.705455
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS -0.736414
TOURISM-ORIENTED INSTITUTIONS 0.855927
AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS 0.801267
CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS 0.770424
ROOMS PER 1000 HABITANTS -0.731045
TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) -0.643715
NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS 0.724497
TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS 0.658017
DENSITY OF ROADS AND HIGHWAYS -0.802719
NUMBER OF CRUISES THAT ARRIVE PER YEAR WORLD CULTURAL HERITAGE SITES 0.612924
CULTURAL SITES LEVEL III (INTERNATIONAL) 0.618247
SEASHORE PROTECTED SITES 0.768414
BIOSHPERE RESERVES 0.574067
NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION 0.611306
SECONDARY ROADS (KMS) % POPULATION ORIENTED TOWARDS TOURISM 0.623938
NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION 0.622606
NUMBER OF INTERNATIONAL BORDER GATES % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP 0.561363
NATIONAL PROTECTED SITES (%) 0.673851
NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) 0.602904
MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS 0.799663
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS 0.639010
DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) 0.664923
ROOMS PER 1000 HABITANTS 0.710062
TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) 0.666603
DENSITY OF ROADS AND HIGHWAYS 0.567876
dtype: float64
In [27]:
# Heatmap of correlations
sns.set(font_scale = 1.2)
f, ax = plt.subplots(figsize = (22, 22))
ax.set_xticklabels(ax.get_xmajorticklabels(), fontsize = 18)
ax.set_yticklabels(ax.get_ymajorticklabels(), fontsize = 18)
sns.heatmap(chile_data_1.drop('Region', axis = 1).corr(), annot = True,
linewidths = 0.5, fmt = '.1f', ax = ax, cmap = "YlGnBu")
Out[27]:
<matplotlib.axes._subplots.AxesSubplot at 0x2097d460220>
In [28]:
# Convert object columns to numeric
# Select columns
cols = ['AVERAGE MONTHLY INCOME (CHILEAN PESOS)',
'NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS',
'NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC.',
'NATIONAL TOURISTS ARRIVALS',
'INTERNATIONAL TOURISTS ARRIVALS',
'NUMBER OF VEHICLES',
'VISITORS TO PROTECTED SITES',
"TOURIST'S ARRIVALS THROUGH BORDER LINES"]
# Convert
chile_data_1[cols] = chile_data_1[cols].apply(pd.to_numeric, errors = 'coerce', axis = 1)
# Now all our columns are integers or floats
2. Exploratory data analysis
In [29]:
plt.barh(sorted(chile_data_1.Region, reverse = True), chile_data_1.MUSEUMS)
plt.title('The number of museums in Chile by region')
plt.show()
In [30]:
plt.scatter(chile_data_1.LIBRARIES, chile_data_1.GALERIES)
plt.title('Correlation between the number of libraries and galeries in Chile by region')
pass
3. Principal Component Analysis
In [31]:
# We need to standardize data for applying PCA
# Create a copy
chile_data_s_1 = chile_data_1.copy()
# Standardize
scaler = StandardScaler()
chile_data_s_1.loc[:, chile_data_s_1.columns != 'Region'] = scaler.fit_transform(chile_data_s_1.loc[:,
chile_data_s_1.columns != 'Region'])
# Set region as an index column
chile_data_s_1 = chile_data_s_1.set_index('Region')
In [32]:
chile_data_s_1
Out[32]:
| CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR | NUMBER OF CULTURAL CENTERS | WORLD CULTURAL HERITAGE SITES | NUMBER OF ARCHEOLOGICAL SITES | NATIONAL MONUMENTS | MUSEUMS | % OF POPULATION THAT ATTENDS MUSEUMS | THEATERS | NUMBER OF THEATER PLAYS PER YEAR | LIBRARIES | GALERIES | % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP | NUMBER OF EXHIBITS | ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR | MAJOR SPORTS EVENTS PER YEAR | OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS | ARTWORK SITES | POPULAR ARCHITECTURE SITES | HISTORICAL SITES | LOCAL MARKETS | CULTURAL SITES LEVEL III (INTERNATIONAL) | CULTURA SITES LEVEL II (NATIONAL) | CULTURAL SITES LEVEL I (LOCAL) | HERITAGE ARCHITECTURAL HOUSES | % OF LAND THAT CORRESPONDS TO FORESTS | NATIONAL PROTECTED SITES (%) | % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS | TOXIC WASTE DISPOSAL (TONS/100 hab.) | NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED | ENVIRONMENTAL ISSUES PER MILLION HABITANTS | NUMBER OF BEACHES AND BEACH RESORTS | LAND AFFECTED BY WILDFIRES | NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) | NATURAL PROTECTED SITES LEVEL II (NATIONAL) | RIVERS, LAKES AND WATERFALLS | MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS | GEISERS AND THERMAL CENTERS | PIERS AND SEASHORES | GLACIERS AND WINTER VACATION LOCATIONS | VALLEYS | DESERTS AND DUNES | ISLANDS AND PENINSULAS | PALEONTOLOGY SITES | HIKING TRAILS | PRESERVED SITES | SEASHORE PROTECTED SITES | BIOSHPERE RESERVES | % AVAILABLE WORKFORCE | % POPULATION ORIENTED TOWARDS TOURISM | AVERAGE MONTHLY INCOME (CHILEAN PESOS) | 5 POPULATION WITH PRIMARY EDUCATION | % POPULATION WITH SECONDARY EDUCATION | AVERAGE NUMBER OF YEARS STUDYING | HIGHER EDUCATION AND TECHNICAL INSTITUTIONS | TOURISM-ORIENTED INSTITUTIONS | NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS | AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS | DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) | CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS | % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR | ROOMS PER 1000 HABITANTS | NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. | TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) | AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR | AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND | NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION | NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS | TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS | TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) | NATIONAL TOURISTS ARRIVALS | INTERNATIONAL TOURISTS ARRIVALS | NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY | DENSITY OF AIRPORTS | DENSITY OF ROADS AND HIGHWAYS | % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) | NUMBER OF VEHICLES | VISITORS TO PROTECTED SITES | NUMBER OF CRUISES THAT ARRIVE PER YEAR | TOURIST'S ARRIVALS THROUGH BORDER LINES | SECONDARY ROADS (KMS) | NUMBER OF INTERNATIONAL BORDER GATES | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Arica y Parinacota | 1.672984 | -0.959349 | -0.559017 | 2.121142 | -0.617597 | -0.721316 | 0.440365 | -0.475143 | -0.335687 | -0.724558 | -0.261024 | 1.389477 | -0.368577 | -0.668153 | -0.454859 | -0.679900 | 0.067176 | 0.326242 | -0.616670 | 0.294619 | 0.360486 | -0.136165 | -0.834058 | -0.355859 | 0.000000 | 0.643982 | -0.248227 | 1.426648 | -0.576151 | -0.410550 | -0.836955 | 0.000000 | 0.597479 | -0.772105 | -0.685892 | 0.015686 | 0.100452 | -0.947748 | -0.755610 | -0.011653 | -0.118729 | -0.580615 | -0.855528 | -0.386889 | 1.657385e-15 | -0.620174 | 0.476731 | -1.851488 | -0.362785 | -0.632189 | 0.646530 | 2.619732 | 1.554178 | 1.133129 | -0.884585 | -0.477620 | -0.477498 | -0.135893 | -0.524853 | 0.790598 | 0.091521 | -1.132711 | 0.620125 | 0.166320 | 0.816497 | -0.347974 | -0.437516 | -0.521005 | -0.482777 | -0.920689 | -0.322261 | 2.514051e+00 | 1.231771 | -0.062345 | -0.946158 | -0.519001 | -0.762949 | 0.621100 | -0.126593 | -0.862524 | 0.528271 |
| Tarapacá | -0.955183 | -0.754748 | 1.118034 | 0.467040 | -0.126575 | -0.599746 | 0.164252 | -0.475143 | -0.289792 | -0.724558 | -0.424164 | 0.064193 | -0.368577 | -0.668153 | -0.454859 | -0.679900 | -0.738938 | 0.453349 | 0.000000 | -0.508888 | 0.094554 | -0.051062 | -0.638575 | -0.688420 | -1.306936 | -0.162045 | -0.490079 | 2.901672 | -0.576151 | -0.410550 | -0.106280 | 0.000000 | -0.942896 | -1.025452 | -0.911185 | -1.160793 | 0.315706 | -0.451833 | -0.755610 | -0.536045 | 0.898951 | -0.060661 | 0.095059 | -0.230042 | -4.306269e-01 | -0.620174 | -0.953463 | 2.567121 | -0.207694 | 0.357940 | -0.936809 | -0.869163 | 0.995791 | 0.344242 | -0.494327 | -0.458377 | -0.451687 | -0.072248 | -0.518277 | 0.696840 | 0.413159 | -0.752896 | 1.147544 | 0.759346 | 0.136083 | -0.347974 | -0.216654 | -0.521005 | -0.425756 | 0.095203 | -0.285528 | -6.097864e-01 | 0.863508 | 1.513222 | -0.955497 | -0.401928 | -0.747731 | -0.614762 | 0.777892 | -0.044781 | -0.792406 |
| Antofagasta | -0.870404 | -0.481948 | -0.559017 | 1.926542 | -0.039282 | 0.737525 | 1.566057 | -0.112746 | -0.274074 | -0.626053 | -0.261024 | -0.650217 | -0.233732 | -0.267261 | -0.454859 | 0.777029 | -0.537409 | -0.182187 | 2.775014 | -0.910642 | -0.082735 | -0.178716 | -0.091225 | -0.333313 | 0.000000 | -0.558761 | -0.490079 | -0.081919 | -0.360095 | -0.410550 | 0.292270 | 0.000000 | -0.522794 | -0.229219 | -0.385501 | 1.662758 | -0.545311 | 1.035911 | -0.610300 | -0.361247 | 1.916631 | 0.199315 | 1.045645 | -0.700582 | -4.306269e-01 | -0.620174 | -0.953463 | 0.175428 | -0.308367 | 1.422279 | -0.936809 | -0.869163 | 0.856194 | 0.200808 | -0.234155 | -0.373331 | -0.361350 | -0.123770 | -0.571981 | 0.912767 | 0.468484 | -0.019562 | 0.746029 | 0.943388 | -0.884538 | -0.771186 | -0.216654 | -0.461755 | -0.405021 | 1.410506 | -0.049429 | -6.005751e-01 | 0.132194 | 0.939456 | -0.721374 | -0.299545 | 1.400719 | -0.614762 | -0.302782 | -0.778986 | 1.848947 |
| Atacama | -0.276947 | -0.413748 | -0.559017 | -0.019460 | -0.541216 | -0.356606 | -0.005664 | -0.595941 | -0.311168 | -0.724558 | -0.587304 | -0.463849 | -0.503423 | -0.668153 | -0.454859 | -0.679900 | -0.738938 | -0.563509 | -0.513892 | -0.910642 | -0.260023 | -0.263819 | -0.834058 | -0.502412 | 0.000000 | -0.609138 | -0.495703 | 0.016253 | -0.792208 | 3.443259 | -0.039855 | 0.000000 | -0.242726 | -0.410181 | -0.836088 | 0.250982 | -0.760565 | 0.374691 | -0.755610 | -0.885639 | 2.679891 | -0.450626 | 0.570352 | -0.386889 | -4.306269e-01 | 0.310087 | -0.953463 | -0.273450 | -0.346460 | 0.341084 | -2.652094 | 0.457025 | 0.437403 | 0.702827 | -1.014671 | -0.504003 | -0.461366 | -0.182215 | -0.464025 | 0.185433 | 0.068078 | -0.872233 | 0.384602 | 0.064074 | -0.544331 | -0.912257 | -0.437516 | -0.521005 | -0.405021 | -0.397285 | -0.431178 | -6.570959e-01 | -0.553020 | 1.191780 | -0.900815 | -0.484583 | -0.692266 | -0.614762 | -0.683313 | -0.832171 | -0.132068 |
| Coquimbo | 0.994747 | -0.618348 | -0.559017 | -0.603261 | -0.355718 | -0.356606 | -0.366735 | -0.354344 | -0.351405 | -0.396208 | -0.261024 | -1.064369 | -0.503423 | -0.267261 | -0.454859 | 2.719600 | -0.537409 | 2.677726 | -0.822226 | -0.508888 | -0.437311 | -0.263819 | -0.052129 | -0.141667 | -1.269757 | -0.709891 | -0.310096 | -1.236250 | -0.576151 | -0.410550 | 1.288645 | 0.223127 | -0.662828 | -0.663528 | -0.460599 | -1.396089 | -0.975820 | 2.193046 | -0.755610 | 1.736318 | -0.627570 | 0.589280 | -0.380235 | -0.700582 | -4.306269e-01 | 1.240347 | 0.476731 | 0.517268 | -0.351901 | -0.131972 | -0.277084 | 0.048967 | -0.400178 | 0.415959 | -0.234155 | -0.267490 | -0.238749 | -0.524389 | -0.328122 | -0.372854 | -0.200109 | 0.348769 | -0.695918 | -0.017723 | 2.517531 | -0.489045 | -0.248206 | 0.816999 | 3.405012 | -0.122213 | -0.367996 | -6.560749e-01 | -0.553020 | -0.243053 | 0.767224 | -0.287633 | -0.459525 | -0.614762 | -0.640329 | 0.157934 | -0.792406 |
| Valparaíso | 0.231731 | 1.427656 | 2.795085 | 1.537341 | 0.833647 | 0.251245 | 1.204986 | -0.173145 | -0.199258 | 0.720180 | 0.554676 | -0.909062 | -0.098887 | 0.534522 | 0.682288 | 1.262672 | -0.335881 | 1.343100 | 1.130561 | 0.696373 | 3.640321 | 2.331823 | 0.299739 | 3.369953 | -0.916559 | -0.565058 | 0.679811 | -0.171909 | 2.016529 | -0.410550 | 2.749996 | 3.392878 | 0.597479 | 1.327055 | -0.760990 | -1.396089 | -0.545311 | 1.035911 | -0.319681 | 2.435507 | -0.373149 | -0.580615 | -0.855528 | 1.495273 | -4.306269e-01 | 2.170608 | 1.906925 | 0.013683 | -0.332855 | -0.433572 | -0.145139 | 0.518234 | 0.856194 | -0.731513 | 1.587050 | 0.688186 | 0.506535 | -0.368515 | 0.380711 | -0.129935 | -0.410158 | 2.026315 | -0.120893 | -0.580075 | -0.204124 | 0.921662 | 0.319723 | 2.685464 | 0.849439 | 1.532153 | 0.074523 | 2.118631e-01 | -0.333213 | -1.671223 | 0.751637 | 0.365125 | 0.180040 | 2.332293 | 0.782855 | -0.456045 | -0.792406 |
| Metropolitana | -0.616065 | 2.723459 | -0.559017 | -0.700561 | 3.485167 | 3.047358 | 0.525323 | 3.632022 | 3.721352 | 3.051461 | 3.491193 | -0.815878 | 3.541940 | -0.668153 | -0.454859 | 0.777029 | -0.335881 | -1.389706 | -0.102778 | 0.696373 | -0.082735 | 2.119066 | 3.271070 | 0.856016 | -0.891773 | -0.678406 | 3.570787 | 0.529198 | 2.664699 | -0.410550 | -1.169080 | -0.677230 | -0.802862 | -1.134029 | -0.986283 | -0.690201 | -0.330057 | -1.278358 | -0.029062 | 0.163144 | -0.627570 | -0.580615 | -0.855528 | -0.700582 | -4.306269e-01 | -0.620174 | -0.953463 | 0.415838 | 1.046635 | 0.809134 | -0.013194 | -0.093853 | 1.693775 | -1.305249 | 2.757824 | 3.460544 | 3.555423 | -0.587648 | 3.548131 | 2.841909 | -1.259731 | 1.953914 | -0.863790 | 2.497524 | 0.476290 | 1.062732 | 3.664195 | 2.107602 | 0.740581 | 1.668068 | 3.668526 | 2.347199e+00 | -0.119431 | -1.226533 | 2.428629 | 3.588997 | -0.270914 | -0.614762 | 3.186253 | 0.248718 | -0.792406 |
| O'Higgins | 1.757764 | -0.209147 | 1.118034 | 0.077840 | -0.192044 | -0.478176 | 0.397886 | 0.189252 | -0.243896 | 0.063481 | -0.587304 | -1.043661 | -0.368577 | 2.138090 | 1.819435 | 0.777029 | 2.485518 | 0.135581 | -0.719448 | -0.508888 | -0.437311 | -0.306371 | -0.521286 | 0.602368 | -0.619129 | -0.558761 | 0.010499 | -1.097174 | 0.072019 | 0.844635 | -0.371980 | 0.821310 | -0.522794 | -0.446373 | -0.911185 | -0.690201 | -0.760565 | -0.947748 | 0.261557 | -0.536045 | -0.118729 | -0.450626 | -0.380235 | 0.397345 | -4.306269e-01 | -0.620174 | -0.953463 | 0.205331 | -0.433528 | -0.469607 | 1.570145 | -1.216013 | -0.679371 | -1.018381 | -0.234155 | -0.428269 | -0.464593 | -0.434389 | 0.153841 | -0.094421 | -0.914651 | -0.468450 | -1.050453 | -2.042190 | 0.476290 | -0.771186 | -0.311309 | -0.486152 | -0.482777 | -1.097303 | -0.470219 | 8.031311e-17 | -0.553020 | -0.709394 | -0.191618 | -0.131441 | -0.791516 | -0.614762 | -0.691941 | -0.600698 | -1.452744 |
| Maule | 1.503425 | 0.063653 | -0.559017 | -0.700561 | -0.333895 | -0.356606 | -1.895977 | -0.052346 | 0.011359 | -0.166364 | -0.424164 | -1.074722 | -0.368577 | 2.138090 | -0.454859 | -0.679900 | 1.276347 | 0.008474 | -0.616670 | 0.294619 | -0.437311 | 0.076593 | -0.013032 | -0.581324 | -0.532379 | -0.697297 | -0.276349 | -0.630042 | 0.288076 | -0.410550 | 0.026570 | -0.196217 | -1.222965 | -0.518758 | 0.740964 | 1.427462 | 0.315706 | -0.451833 | 0.116248 | 0.862333 | 0.135691 | -0.580615 | -0.855528 | 0.711039 | -4.306269e-01 | -0.620174 | -0.953463 | -0.898684 | -0.536922 | -1.189355 | 0.184723 | 0.436622 | -1.377355 | -0.946664 | -0.624413 | -0.424545 | -0.393613 | -0.472778 | 0.171651 | -0.516332 | -1.093755 | -0.655528 | -0.904505 | -0.712994 | -0.204124 | -0.630116 | -0.090448 | -0.521005 | -0.482777 | -0.406235 | -0.460022 | -6.528849e-01 | -0.553020 | -0.602802 | 0.215118 | -0.061316 | -0.407268 | -0.614762 | -0.689057 | 0.177622 | -0.132068 |
| Biobío | -0.531285 | 1.086656 | -0.559017 | -0.797861 | -0.279337 | 1.223806 | -0.239298 | 0.128852 | -0.094893 | 0.950024 | 0.554676 | -0.836585 | 0.845031 | -0.668153 | -0.454859 | -0.679900 | 0.067176 | -0.881277 | 1.336118 | -0.107134 | -0.437311 | -1.072298 | 1.003476 | 0.072525 | -0.036662 | -0.558761 | 0.032997 | -0.726577 | -0.360095 | 0.638706 | 0.491545 | -0.251719 | -0.802862 | -0.989259 | 0.440573 | -0.690201 | -0.760565 | -0.617138 | -0.319681 | -1.060436 | -0.627570 | -0.450626 | 2.946818 | -0.700582 | -4.306269e-01 | -0.620174 | -0.953463 | -0.815943 | -0.267554 | -0.669181 | -0.277084 | 0.028564 | -0.120984 | -1.018381 | 1.066706 | 0.711154 | 0.519440 | -0.683383 | 0.017116 | -0.296143 | -1.203468 | 0.053838 | -0.953363 | 0.064074 | -0.544331 | -0.347974 | -0.153551 | -0.433872 | -0.436124 | 1.259932 | -0.337412 | -6.594967e-01 | -0.553020 | -0.985035 | 1.524147 | 0.350357 | -0.512695 | -0.614762 | -0.689141 | 2.043257 | -0.792406 |
| Araucanía | 0.570849 | -0.209147 | -0.559017 | -0.603261 | 0.124392 | -0.599746 | -1.449948 | -0.112746 | -0.289163 | 0.785850 | 0.065256 | 1.876105 | -0.503423 | 1.336306 | 2.956582 | -0.679900 | 1.880932 | -0.690616 | -0.411113 | 3.106895 | -0.437311 | -0.987195 | 0.104257 | -0.575688 | 0.502429 | -0.130560 | -0.265100 | -0.308531 | 0.504132 | -0.410550 | 0.491545 | -0.603228 | 1.857786 | 1.616595 | 2.843699 | 0.956870 | 2.468250 | -0.782443 | 0.261557 | 0.337941 | -0.627570 | -0.450626 | 0.570352 | 2.906894 | -4.306269e-01 | -0.620174 | 0.476731 | -0.199033 | 0.023581 | -1.112122 | 0.910420 | 0.212190 | -1.237758 | -0.588079 | -0.104069 | -0.317462 | -0.296823 | -0.461338 | -0.428131 | -0.982281 | -0.737422 | 0.498065 | -0.439725 | 0.207218 | 1.496910 | 1.062732 | -0.311309 | -0.437358 | -0.353184 | -0.162528 | -0.298595 | -3.030555e-01 | -0.553020 | -0.490380 | 0.468371 | -0.259311 | 0.775251 | -0.614762 | -0.312432 | 1.257007 | 0.528271 |
| Los Ríos | -0.700845 | -0.481948 | -0.559017 | -0.700561 | -0.563039 | -0.235036 | -0.812764 | -0.414743 | -0.342603 | -0.724558 | -0.261024 | 0.488698 | -0.233732 | -0.267261 | -0.454859 | -0.194257 | -0.738938 | -1.071938 | -0.513892 | -0.107134 | -0.437311 | -1.072298 | -0.364900 | -0.378406 | 1.537238 | -0.300581 | -0.332594 | -0.421428 | -0.792208 | -0.410550 | -0.504830 | -0.615562 | -0.942896 | -0.120641 | -0.010013 | -0.690201 | 1.822487 | -0.617138 | -0.755610 | -1.235233 | -0.627570 | 0.069327 | -0.380235 | -0.700582 | 1.657385e-15 | -0.620174 | 0.476731 | -1.059410 | -0.490667 | -0.914058 | 0.316668 | -1.256818 | -0.958565 | -0.444645 | -0.624413 | -0.399714 | -0.325860 | -0.472837 | -0.384566 | -0.119991 | 0.107463 | -0.608259 | -0.707819 | 0.258341 | -1.224745 | 0.216308 | -0.500619 | -0.332450 | -0.482777 | -0.799543 | -0.421096 | -6.330079e-01 | -0.553020 | -0.153948 | -0.201151 | -0.497619 | -0.868449 | 0.050702 | -0.691941 | -0.689341 | -0.792406 |
| Los Lagos | -0.870404 | 0.541054 | 1.118034 | -0.797861 | -0.224779 | 0.129675 | -0.048143 | -0.112746 | -0.230693 | -0.100694 | -0.424164 | 0.913203 | -0.233732 | -0.668153 | -0.454859 | -0.679900 | -0.738938 | 0.707564 | -0.719448 | 0.294619 | -0.437311 | 0.927623 | 0.065161 | -0.158577 | 2.169276 | 0.266157 | -0.405712 | -0.473786 | -0.576151 | -0.410550 | 0.026570 | -0.701897 | 0.457445 | 0.241283 | 0.290378 | 0.956870 | 0.746215 | 0.374691 | -0.319681 | -0.361247 | -0.627570 | 3.449024 | -0.855528 | -0.700582 | 3.301473e+00 | 2.170608 | 0.476731 | 0.187151 | 3.470945 | -0.657185 | -0.145139 | -0.869163 | -1.237758 | -0.444645 | 0.156103 | -0.271835 | -0.280691 | -0.109448 | 0.109727 | -0.574576 | 0.581948 | 1.280998 | 0.044474 | -0.385808 | -0.884538 | 2.755581 | -0.122000 | -0.332450 | -0.233959 | 0.387483 | 0.064338 | 5.209139e-02 | 3.146505 | -0.258875 | -0.185306 | -0.243263 | 2.629382 | 2.047094 | -0.039476 | 2.124835 | 0.528271 |
| Aysén | -0.955183 | -0.822948 | -0.559017 | -0.603261 | -0.737624 | -1.207597 | -1.110116 | -0.595941 | -0.370895 | -0.724558 | -0.587304 | 1.016741 | -0.503423 | -0.668153 | -0.454859 | -0.679900 | -0.738938 | -0.182187 | -0.822226 | -0.910642 | -0.348667 | -0.646783 | -0.716768 | -0.688420 | 1.431898 | 1.745972 | -0.495703 | 0.272316 | -0.792208 | -0.410550 | -1.169080 | -0.695731 | 1.717752 | 2.123289 | 0.891159 | 0.486278 | 0.100452 | -0.451833 | 2.586510 | -0.885639 | -0.627570 | 0.459292 | 0.570352 | 0.240498 | 1.435423e+00 | -0.620174 | 1.906925 | 1.284880 | -0.316530 | 0.770348 | 1.438200 | -0.501911 | -0.679371 | 2.065449 | -0.754499 | -0.517660 | -0.493630 | 1.282215 | -0.599654 | -1.604493 | 1.860058 | -1.009879 | -0.003132 | -1.398041 | -1.224745 | -1.053328 | -0.500619 | -0.521005 | -0.384286 | -1.428005 | -0.450049 | -4.129872e-01 | -0.553020 | 1.569849 | -1.034075 | -0.603383 | -0.640792 | -0.614762 | -0.501923 | -0.861020 | 1.188609 |
| Magallanes y Antártica | -0.955183 | -0.891148 | -0.559017 | -0.603261 | -0.432100 | -0.478176 | 1.629775 | -0.475143 | -0.399186 | -0.658888 | -0.587304 | 1.109925 | -0.098887 | -0.668153 | -0.454859 | -0.679900 | -0.335881 | -0.690616 | 0.616670 | -0.910642 | -0.260023 | -0.476577 | -0.677672 | -0.496775 | -0.067645 | 2.873150 | -0.484454 | 0.001527 | -0.144038 | -0.410550 | -1.169080 | -0.695731 | 1.437684 | 1.001324 | 0.740964 | 0.956870 | -1.191074 | 1.531826 | 2.150581 | 0.337941 | -0.627570 | -0.580615 | -0.380235 | -0.543736 | -4.306269e-01 | 0.310087 | 0.476731 | -0.268692 | -0.585898 | 2.508457 | 0.316668 | 1.354752 | 0.297807 | 1.635147 | -0.364241 | -0.419578 | -0.335539 | 3.346638 | -0.561569 | -0.736521 | 2.228582 | -0.642380 | 2.796824 | 0.176544 | -0.204124 | -0.347974 | -0.437516 | -0.521005 | -0.420572 | -1.019545 | 0.086400 | 5.976015e-02 | -0.497172 | 1.189281 | -1.019132 | -0.515456 | 1.168712 | 1.096431 | 0.621928 | -0.883807 | 1.848947 |
3.1. Eigenvalues and eigenvectors
In [33]:
# Calculate eigenvalues and vectors
cov_mat = np.cov(chile_data_s_1.T)
eig_val, eig_vec = np.linalg.eig(cov_mat)
# Print
print('Eigenvectors \n%s' %eig_vec)
print('\nEigenvalues \n%s' %eig_val)
Eigenvectors [[-0.00326345+0.j 0.20824017+0.j -0.06726017+0.j ... 0.10465649+0.06290102j 0.0825293 +0.j -0.06441922+0.j ] [ 0.18406724+0.j 0.02020972+0.j 0.00790098+0.j ... 0.01160419-0.03416025j -0.06176204+0.j 0.0253879 +0.j ] [ 0.03833065+0.j 0.12890046+0.j 0.19880302+0.j ... 0.0680857 -0.08400698j 0.10493056+0.j 0.00281664+0.j ] ... [ 0.15581808+0.j -0.13450585+0.j 0.07284215+0.j ... 0.06271399+0.12335499j -0.15264865+0.j -0.19538787+0.j ] [ 0.05190728+0.j 0.07724022+0.j -0.05197245+0.j ... -0.11024802+0.06092036j 0.06548136+0.j 0.03261723+0.j ] [-0.09074038+0.j -0.16095204+0.j 0.08978727+0.j ... 0.05759012-0.03912584j -0.2056735 +0.j -0.00869763+0.j ]] Eigenvalues [ 2.69523928e+01+0.00000000e+00j 1.11549471e+01+0.00000000e+00j 9.96822553e+00+0.00000000e+00j 9.61463611e+00+0.00000000e+00j 6.68175566e+00+0.00000000e+00j 4.31458703e+00+0.00000000e+00j 4.23410198e+00+0.00000000e+00j 3.18910023e+00+0.00000000e+00j 2.65843378e+00+0.00000000e+00j 2.44249475e+00+0.00000000e+00j 1.56101521e+00+0.00000000e+00j 1.03135070e+00+0.00000000e+00j 1.22663898e+00+0.00000000e+00j 1.75603441e+00+0.00000000e+00j -1.32494920e-15+0.00000000e+00j 1.07942003e-15+2.19847086e-16j 1.07942003e-15-2.19847086e-16j -1.08558207e-15+7.71474565e-17j -1.08558207e-15-7.71474565e-17j 8.71749523e-16+0.00000000e+00j -8.76887078e-16+1.29909988e-16j -8.76887078e-16-1.29909988e-16j -8.19629336e-16+0.00000000e+00j -8.02835427e-16+0.00000000e+00j 6.42085919e-16+2.25477533e-16j 6.42085919e-16-2.25477533e-16j 7.31410794e-16+0.00000000e+00j 6.75212761e-16+9.97428658e-17j 6.75212761e-16-9.97428658e-17j -6.16515505e-16+1.93020234e-16j -6.16515505e-16-1.93020234e-16j 3.37982701e-16+4.58297866e-16j 3.37982701e-16-4.58297866e-16j -5.40920802e-16+2.46405795e-16j -5.40920802e-16-2.46405795e-16j 7.96558550e-19+5.16308390e-16j 7.96558550e-19-5.16308390e-16j 5.33183976e-16+1.90833605e-16j 5.33183976e-16-1.90833605e-16j -6.12695112e-16+3.54095714e-17j -6.12695112e-16-3.54095714e-17j 5.44210923e-16+0.00000000e+00j -4.32965571e-16+2.70857102e-16j -4.32965571e-16-2.70857102e-16j 3.16516089e-16+3.17529050e-16j 3.16516089e-16-3.17529050e-16j 4.90988752e-16+0.00000000e+00j 3.26653526e-16+2.29722921e-16j 3.26653526e-16-2.29722921e-16j -4.57634772e-16+8.29659489e-17j -4.57634772e-16-8.29659489e-17j 4.31151588e-16+0.00000000e+00j -3.89811887e-16+1.55411110e-16j -3.89811887e-16-1.55411110e-16j 3.62866586e-16+1.35658313e-16j 3.62866586e-16-1.35658313e-16j -4.10026407e-16+0.00000000e+00j -1.56552748e-16+2.73925233e-16j -1.56552748e-16-2.73925233e-16j -2.11588101e-16+1.70609867e-16j -2.11588101e-16-1.70609867e-16j 3.85920978e-17+2.51322967e-16j 3.85920978e-17-2.51322967e-16j 7.81144645e-17+2.48725863e-16j 7.81144645e-17-2.48725863e-16j 2.26001301e-16+1.39886408e-16j 2.26001301e-16-1.39886408e-16j -6.68674770e-17+1.87675760e-16j -6.68674770e-17-1.87675760e-16j -2.53426156e-16+6.02657924e-17j -2.53426156e-16-6.02657924e-17j 2.74138192e-16+0.00000000e+00j -8.60230185e-17+1.23946681e-16j -8.60230185e-17-1.23946681e-16j -2.36499805e-16+0.00000000e+00j 7.46164047e-17+8.22653204e-17j 7.46164047e-17-8.22653204e-17j 1.64379319e-16+6.55327386e-17j 1.64379319e-16-6.55327386e-17j 2.16307442e-16+0.00000000e+00j -1.45515467e-17+0.00000000e+00j]
In [34]:
# Run PCA and fit the model
myPCA = PCA()
x = myPCA.fit(chile_data_s_1)
# Plotting the varaince explained by each component
plt.bar(range(1,len(x.explained_variance_ )+1),x.explained_variance_ratio_)
plt.ylabel('Explained variance')
plt.xlabel('Components')
plt.title('All Principle Components')
pass
In [35]:
# Deciding on the number of principal componenets to chose
plt.plot(range(1, len(x.explained_variance_)+1), x.explained_variance_ratio_.cumsum())
plt.ylabel('Explained variance')
plt.xlabel('Components')
pass
In [36]:
# Calculate the numeric values of principal components
x.explained_variance_ratio_.cumsum()
Out[36]:
array([0.31056255, 0.43909692, 0.55395713, 0.66474306, 0.74173449,
0.79144989, 0.8402379 , 0.87698473, 0.90761689, 0.93576086,
0.95599501, 0.97398201, 0.98811612, 1. , 1. ])
will use only first 7 components, which explain 84% of variance.
In [37]:
# Calculate loadings
myPCA = PCA(n_components = 7)
pca_model = myPCA.fit(chile_data_s_1)
# Print
print("The loadings are are \n {}".format(pca_model.components_))
The loadings are are [[-3.26344655e-03 1.84067239e-01 3.83306466e-02 -7.23394920e-03 1.89367967e-01 1.77718705e-01 4.28888502e-02 1.80816408e-01 1.75744035e-01 1.83273803e-01 1.89226250e-01 -9.19233750e-02 1.76736744e-01 -2.33968407e-03 3.44343546e-03 8.24000942e-02 1.57056623e-04 -2.43602829e-02 3.35780487e-02 6.56190691e-02 6.30485406e-02 1.46874937e-01 1.86464208e-01 1.19261160e-01 -8.06108890e-02 -9.44997273e-02 1.89172194e-01 -2.84345477e-03 1.72731180e-01 -2.65307899e-02 3.39580094e-02 4.57788989e-02 -6.73488248e-02 -6.92156625e-02 -6.72298110e-02 -7.84966666e-02 -2.81542402e-02 -4.29849694e-02 -5.47581663e-02 6.40633176e-02 -4.27780975e-02 -3.18548698e-02 -3.52051754e-02 -6.30454443e-03 -4.07223535e-02 1.97763697e-02 -4.31746880e-02 6.90941735e-03 5.62308924e-02 -8.62514507e-03 -1.41202049e-02 -8.72500998e-03 8.70796084e-02 -1.30382323e-01 1.85947486e-01 1.90520746e-01 1.88437411e-01 -8.47907807e-02 1.86791012e-01 1.45137261e-01 -1.22095527e-01 1.57441862e-01 -8.17256553e-02 1.13569258e-01 4.17261941e-02 8.50647027e-02 1.85656621e-01 1.58968571e-01 6.89541575e-02 1.47490361e-01 1.73481294e-01 1.05107949e-01 -3.90739108e-03 -1.35187201e-01 1.71388118e-01 1.90228008e-01 -5.07229601e-03 8.52520439e-03 1.55818082e-01 5.19072846e-02 -9.07403778e-02] [-2.08240165e-01 -2.02097221e-02 -1.28900460e-01 7.74734314e-04 5.30581710e-02 8.25267486e-02 1.23135676e-01 7.48874275e-02 1.03220440e-01 -1.54906591e-02 6.19211778e-02 8.12451535e-02 1.16894851e-01 -2.25797775e-01 -1.86706465e-01 -1.07869203e-01 -1.80189332e-01 -1.51918353e-01 6.63902463e-02 -1.41930363e-01 -9.54951885e-02 -5.28439294e-03 3.61324473e-02 -1.24732327e-01 5.17274035e-02 1.59198578e-01 5.32312233e-02 1.55010260e-01 -4.15627603e-02 5.35670380e-03 -2.19020425e-01 -1.73893343e-01 2.11969599e-02 -3.89465539e-02 -5.42563793e-02 8.45072909e-02 -7.12537301e-02 -8.09121618e-03 9.03703911e-02 -1.50497141e-01 7.28692103e-02 1.47887169e-02 3.35097324e-02 -1.91659117e-01 4.06755844e-02 -8.77120365e-02 -5.76503597e-02 5.87925994e-02 3.94811941e-02 2.37121544e-01 -7.92609574e-02 2.80317499e-02 1.53466243e-01 1.54650405e-01 -1.54682334e-04 6.59307916e-02 7.83134209e-02 1.68524835e-01 4.72593885e-02 1.18805017e-01 1.72851125e-01 -6.83313500e-02 1.88016314e-01 1.68548102e-01 -1.23141097e-01 -3.84432296e-02 8.36974143e-02 -6.09562650e-02 -9.05656822e-02 8.74288007e-04 1.28298985e-01 8.66347125e-02 6.04743366e-02 1.89216680e-01 -8.18407607e-02 6.28094248e-02 4.49248636e-02 -2.81003694e-02 1.34505855e-01 -7.72402159e-02 1.60952045e-01] [-6.72601655e-02 7.90098232e-03 1.98803016e-01 1.31476712e-01 3.69730413e-03 -3.78539934e-02 1.89921617e-01 -7.47860172e-02 -6.72295511e-02 -5.13379054e-02 -3.54471625e-02 3.00881006e-02 -6.24193209e-02 -9.30019512e-02 -4.20568338e-02 1.01573124e-01 -1.47468750e-01 1.85525172e-01 8.13875229e-02 -4.14386467e-02 2.29441917e-01 1.76852823e-01 -5.40488637e-02 1.76054194e-01 -6.62647174e-03 1.04752568e-01 -2.34208709e-02 2.84242076e-02 5.54101307e-02 -8.59056349e-02 1.48462621e-01 1.82995339e-01 1.32801884e-01 1.37523500e-01 -6.80081341e-02 -3.71006296e-02 -9.59559255e-02 2.19110135e-01 3.42115504e-02 1.88197187e-01 -2.67826407e-02 1.05071846e-01 -1.21039360e-01 4.01040532e-03 9.50459774e-02 2.63808922e-01 2.07325056e-01 6.38975020e-02 7.41321871e-02 1.05659647e-01 -3.26727631e-02 6.75573552e-02 8.88663926e-02 1.02859680e-01 5.02224174e-02 -1.90061348e-02 -2.55108858e-02 1.18121954e-01 -4.00153294e-02 -4.03368954e-02 1.56617370e-01 1.34124327e-01 1.57058200e-01 -2.60714401e-02 -1.22937113e-02 1.08066972e-01 -2.48587318e-02 1.54047690e-01 1.07766445e-01 6.75968476e-02 -5.74014818e-03 3.18904190e-02 1.12930008e-01 8.56277664e-03 -6.12990594e-02 -4.54109376e-02 1.56034894e-01 2.65616594e-01 7.28421502e-02 -5.19724521e-02 8.97872670e-02] [-6.45423962e-02 5.95374302e-02 -1.71041361e-02 -1.62238884e-01 3.15601573e-02 -2.13512914e-03 -1.24976800e-01 4.69921394e-02 3.74635389e-02 8.55147505e-02 3.64067372e-02 2.14585936e-01 3.38010189e-02 1.91064847e-02 1.00173332e-01 -1.36514347e-01 5.22993453e-02 -9.27257198e-02 -1.01084699e-01 1.88858826e-01 -6.97056830e-02 1.52660980e-03 6.28418534e-02 -4.50783226e-02 2.32917753e-01 1.21895705e-01 2.53243417e-02 -5.55061148e-02 3.68070002e-02 -1.32921127e-01 -8.23128429e-02 -1.40639416e-01 1.89322840e-01 1.79082873e-01 2.35793220e-01 1.33962694e-01 2.05918374e-01 -9.41100376e-02 1.44024502e-01 -3.58024399e-02 -2.03022782e-01 1.58871209e-01 -4.29215340e-02 1.11032143e-01 2.16521511e-01 3.70873748e-02 1.33192101e-01 -2.82453000e-02 2.03207597e-01 -6.72388765e-02 1.72390119e-01 -2.64943989e-02 -1.70382698e-01 -2.87144534e-02 4.56955770e-02 2.82103348e-02 3.20553141e-02 5.78851272e-02 4.52975095e-02 -1.18673897e-01 4.70016257e-02 9.55299080e-02 -3.11045462e-02 -2.44852636e-02 -4.59169810e-02 2.26577087e-01 3.14479942e-02 -3.07741107e-02 -8.10965789e-02 -3.81898141e-02 5.14639000e-02 4.31272548e-02 1.14647335e-01 -5.47894180e-02 4.32559716e-02 2.86044583e-02 1.75886080e-01 1.01358533e-01 3.29161248e-02 1.74510248e-01 1.00237393e-01] [-1.30025827e-01 4.79638221e-02 5.57893243e-02 -6.32476877e-02 -5.55461595e-02 7.53353756e-02 -2.82412215e-02 -3.28024643e-02 -4.17442054e-02 -4.86065853e-02 -4.63287467e-02 -9.56637812e-02 -2.14983719e-02 -1.15097176e-01 -1.52217580e-01 3.78206056e-03 -1.56952560e-01 7.47221890e-02 2.12396622e-02 -8.69006329e-02 -8.77618783e-02 1.13707370e-02 1.79588152e-02 -5.36381763e-02 1.27578546e-01 -1.71463188e-01 -7.15120310e-02 -4.93076947e-02 -1.43359630e-01 7.31011380e-02 7.84979822e-02 -5.78989002e-02 -1.93679786e-01 -1.61041009e-01 -9.35810488e-02 -7.72249338e-03 2.40850885e-02 5.67115840e-02 -2.19835247e-01 -1.20212366e-01 9.51200908e-02 2.92351210e-01 7.24369841e-02 -1.91688511e-01 2.02828798e-01 1.40896723e-01 -1.16127731e-01 4.88269718e-02 2.40713894e-01 -1.01004777e-01 -2.00042096e-01 -2.06805989e-01 -8.87605497e-02 -1.12327980e-01 -5.15353171e-03 -3.14780450e-02 -3.62873900e-02 -1.69869628e-01 -2.10582060e-02 2.28565351e-02 -4.68657634e-02 6.88505272e-02 -8.65079506e-02 3.28861494e-02 -1.19196174e-01 1.40264258e-01 -3.60690143e-02 -5.69582146e-02 2.29101666e-02 1.32420648e-01 -4.34450485e-02 -1.39676614e-01 2.23833851e-01 2.00050105e-03 2.02085637e-02 -3.42119704e-02 1.24280473e-01 3.76290219e-02 -8.65122630e-02 2.05318854e-01 -7.41860679e-02] [ 1.70865637e-01 -5.24306781e-02 1.11683862e-01 2.48104717e-01 7.06632657e-04 -7.89865929e-02 1.34120994e-02 -1.08692319e-02 7.20629488e-03 -5.79870767e-02 -1.56432937e-02 1.60007887e-01 -3.95252669e-02 3.13525238e-03 1.51312241e-02 -1.55497351e-01 4.06797961e-02 7.67813066e-03 -1.55448912e-01 1.15297849e-01 8.15280367e-02 9.94006860e-02 -8.98752222e-02 -6.51102037e-03 1.54082936e-02 -4.50085978e-02 1.01117121e-02 2.88374610e-01 -8.39667145e-03 -2.18925358e-02 -8.89539908e-02 3.57721367e-02 -1.90560583e-02 -1.41161437e-01 -1.47248934e-01 -2.49843371e-02 1.63054314e-01 -2.36004306e-01 -2.11429665e-01 -4.56346357e-02 6.44426569e-02 7.73887558e-03 -2.34370851e-01 2.11097460e-02 6.39881243e-02 -4.50073947e-02 -2.80087577e-02 -1.07555773e-01 9.12223140e-02 -1.78647824e-01 1.25928512e-02 1.59005150e-01 1.59305761e-01 -1.10100071e-02 -9.04455556e-02 -4.47803197e-02 -4.15437192e-02 -1.33181037e-01 5.45171016e-03 1.76787681e-01 -6.47882652e-02 -7.03225035e-02 3.89210869e-02 4.25258313e-02 3.28684656e-02 1.07800592e-01 3.33993914e-03 -4.68640047e-02 -1.62650336e-01 -7.71223419e-02 3.23331371e-03 2.87061088e-01 2.80515238e-01 -2.65800917e-02 -1.35703125e-01 -2.35915252e-02 -6.41233221e-02 1.19918064e-01 6.57146404e-02 -6.28100220e-02 -6.33159847e-02] [ 1.20464630e-01 -7.46553822e-02 -7.51619332e-02 -1.86715041e-01 -8.29413954e-03 -8.17796695e-02 -1.21057153e-01 4.75947615e-02 7.20346262e-02 -4.56441906e-02 2.10313363e-03 -2.34652590e-02 2.38044779e-02 -6.20980447e-02 -1.46389498e-01 1.81334407e-01 -7.02125160e-02 2.17458770e-01 -3.55255485e-01 -1.30394188e-01 -1.46470771e-01 8.42133040e-03 -5.35288594e-03 -8.62353395e-02 -3.63579821e-02 7.01325809e-02 5.06739348e-02 -3.99864764e-02 -7.68733445e-02 -9.07140465e-02 -1.31227949e-01 -1.08255975e-01 -6.10646139e-02 -9.15221933e-02 -1.16876383e-01 -1.87683068e-01 -7.32499752e-02 5.40360432e-02 5.92851179e-02 7.07697779e-02 -2.07401848e-01 1.22464381e-01 -2.37120088e-01 -1.78809232e-01 1.09080705e-01 6.82765931e-02 9.72828278e-02 8.19004333e-02 5.01988819e-02 -1.32576663e-02 1.39531200e-01 2.43626542e-03 -6.56097478e-02 1.16643544e-01 -5.98768957e-02 1.30544430e-02 2.92852371e-02 3.51529163e-02 7.21381864e-02 -1.75581311e-02 6.22539702e-02 -5.30345122e-02 -8.68981784e-02 -6.63686340e-02 2.02884076e-01 -3.29913822e-02 5.54141357e-02 4.91136406e-02 2.90705254e-01 -2.20511164e-01 5.34806379e-02 9.22377240e-02 2.96021756e-02 1.66449424e-02 2.63053098e-02 3.23301814e-02 -1.57209186e-01 -4.61633078e-02 2.16461926e-02 -5.64628530e-02 -1.37124824e-01]]
In [38]:
# Explore the importance of each feature for principle components
pca = PCA(n_components = 7).fit(chile_data_s_1)
vars = pca.explained_variance_ratio_
c_names = chile_data_s_1.columns
sum = 0
print('Variance: Projected dimension')
print('------------------------------')
for idx, row in enumerate(pca.components_):
output = '{0:4.1f}%: '.format(100.0 * vars[idx])
output += " + ".join("{0:5.2f} * {1:s}".format(val, name) \
for val, name in zip(row, c_names))
sum += 100*vars[idx]
print(output)
print('Total variance explained by the 7 components {0:4.1f}%'.format(sum))
# Total variance explained by the 7 components 84.0%
Variance: Projected dimension ------------------------------ 31.1%: -0.00 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + 0.18 * NUMBER OF CULTURAL CENTERS + 0.04 * WORLD CULTURAL HERITAGE SITES + -0.01 * NUMBER OF ARCHEOLOGICAL SITES + 0.19 * NATIONAL MONUMENTS + 0.18 * MUSEUMS + 0.04 * % OF POPULATION THAT ATTENDS MUSEUMS + 0.18 * THEATERS + 0.18 * NUMBER OF THEATER PLAYS PER YEAR + 0.18 * LIBRARIES + 0.19 * GALERIES + -0.09 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + 0.18 * NUMBER OF EXHIBITS + -0.00 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + 0.00 * MAJOR SPORTS EVENTS PER YEAR + 0.08 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + 0.00 * ARTWORK SITES + -0.02 * POPULAR ARCHITECTURE SITES + 0.03 * HISTORICAL SITES + 0.07 * LOCAL MARKETS + 0.06 * CULTURAL SITES LEVEL III (INTERNATIONAL) + 0.15 * CULTURA SITES LEVEL II (NATIONAL) + 0.19 * CULTURAL SITES LEVEL I (LOCAL) + 0.12 * HERITAGE ARCHITECTURAL HOUSES + -0.08 * % OF LAND THAT CORRESPONDS TO FORESTS + -0.09 * NATIONAL PROTECTED SITES (%) + 0.19 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + -0.00 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + 0.17 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.03 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + 0.03 * NUMBER OF BEACHES AND BEACH RESORTS + 0.05 * LAND AFFECTED BY WILDFIRES + -0.07 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.07 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.07 * RIVERS, LAKES AND WATERFALLS + -0.08 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.03 * GEISERS AND THERMAL CENTERS + -0.04 * PIERS AND SEASHORES + -0.05 * GLACIERS AND WINTER VACATION LOCATIONS + 0.06 * VALLEYS + -0.04 * DESERTS AND DUNES + -0.03 * ISLANDS AND PENINSULAS + -0.04 * PALEONTOLOGY SITES + -0.01 * HIKING TRAILS + -0.04 * PRESERVED SITES + 0.02 * SEASHORE PROTECTED SITES + -0.04 * BIOSHPERE RESERVES + 0.01 * % AVAILABLE WORKFORCE + 0.06 * % POPULATION ORIENTED TOWARDS TOURISM + -0.01 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.01 * 5 POPULATION WITH PRIMARY EDUCATION + -0.01 * % POPULATION WITH SECONDARY EDUCATION + 0.09 * AVERAGE NUMBER OF YEARS STUDYING + -0.13 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + 0.19 * TOURISM-ORIENTED INSTITUTIONS + 0.19 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + 0.19 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + -0.08 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + 0.19 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + 0.15 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + -0.12 * ROOMS PER 1000 HABITANTS + 0.16 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + -0.08 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + 0.11 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + 0.04 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + 0.09 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + 0.19 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + 0.16 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + 0.07 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + 0.15 * NATIONAL TOURISTS ARRIVALS + 0.17 * INTERNATIONAL TOURISTS ARRIVALS + 0.11 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + -0.00 * DENSITY OF AIRPORTS + -0.14 * DENSITY OF ROADS AND HIGHWAYS + 0.17 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + 0.19 * NUMBER OF VEHICLES + -0.01 * VISITORS TO PROTECTED SITES + 0.01 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + 0.16 * TOURIST'S ARRIVALS THROUGH BORDER LINES + 0.05 * SECONDARY ROADS (KMS) + -0.09 * NUMBER OF INTERNATIONAL BORDER GATES 12.9%: -0.21 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + -0.02 * NUMBER OF CULTURAL CENTERS + -0.13 * WORLD CULTURAL HERITAGE SITES + 0.00 * NUMBER OF ARCHEOLOGICAL SITES + 0.05 * NATIONAL MONUMENTS + 0.08 * MUSEUMS + 0.12 * % OF POPULATION THAT ATTENDS MUSEUMS + 0.07 * THEATERS + 0.10 * NUMBER OF THEATER PLAYS PER YEAR + -0.02 * LIBRARIES + 0.06 * GALERIES + 0.08 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + 0.12 * NUMBER OF EXHIBITS + -0.23 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + -0.19 * MAJOR SPORTS EVENTS PER YEAR + -0.11 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.18 * ARTWORK SITES + -0.15 * POPULAR ARCHITECTURE SITES + 0.07 * HISTORICAL SITES + -0.14 * LOCAL MARKETS + -0.10 * CULTURAL SITES LEVEL III (INTERNATIONAL) + -0.01 * CULTURA SITES LEVEL II (NATIONAL) + 0.04 * CULTURAL SITES LEVEL I (LOCAL) + -0.12 * HERITAGE ARCHITECTURAL HOUSES + 0.05 * % OF LAND THAT CORRESPONDS TO FORESTS + 0.16 * NATIONAL PROTECTED SITES (%) + 0.05 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + 0.16 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + -0.04 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + 0.01 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + -0.22 * NUMBER OF BEACHES AND BEACH RESORTS + -0.17 * LAND AFFECTED BY WILDFIRES + 0.02 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.04 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.05 * RIVERS, LAKES AND WATERFALLS + 0.08 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.07 * GEISERS AND THERMAL CENTERS + -0.01 * PIERS AND SEASHORES + 0.09 * GLACIERS AND WINTER VACATION LOCATIONS + -0.15 * VALLEYS + 0.07 * DESERTS AND DUNES + 0.01 * ISLANDS AND PENINSULAS + 0.03 * PALEONTOLOGY SITES + -0.19 * HIKING TRAILS + 0.04 * PRESERVED SITES + -0.09 * SEASHORE PROTECTED SITES + -0.06 * BIOSHPERE RESERVES + 0.06 * % AVAILABLE WORKFORCE + 0.04 * % POPULATION ORIENTED TOWARDS TOURISM + 0.24 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.08 * 5 POPULATION WITH PRIMARY EDUCATION + 0.03 * % POPULATION WITH SECONDARY EDUCATION + 0.15 * AVERAGE NUMBER OF YEARS STUDYING + 0.15 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + -0.00 * TOURISM-ORIENTED INSTITUTIONS + 0.07 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + 0.08 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + 0.17 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + 0.05 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + 0.12 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + 0.17 * ROOMS PER 1000 HABITANTS + -0.07 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + 0.19 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + 0.17 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + -0.12 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + -0.04 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + 0.08 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + -0.06 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + -0.09 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + 0.00 * NATIONAL TOURISTS ARRIVALS + 0.13 * INTERNATIONAL TOURISTS ARRIVALS + 0.09 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + 0.06 * DENSITY OF AIRPORTS + 0.19 * DENSITY OF ROADS AND HIGHWAYS + -0.08 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + 0.06 * NUMBER OF VEHICLES + 0.04 * VISITORS TO PROTECTED SITES + -0.03 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + 0.13 * TOURIST'S ARRIVALS THROUGH BORDER LINES + -0.08 * SECONDARY ROADS (KMS) + 0.16 * NUMBER OF INTERNATIONAL BORDER GATES 11.5%: -0.07 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + 0.01 * NUMBER OF CULTURAL CENTERS + 0.20 * WORLD CULTURAL HERITAGE SITES + 0.13 * NUMBER OF ARCHEOLOGICAL SITES + 0.00 * NATIONAL MONUMENTS + -0.04 * MUSEUMS + 0.19 * % OF POPULATION THAT ATTENDS MUSEUMS + -0.07 * THEATERS + -0.07 * NUMBER OF THEATER PLAYS PER YEAR + -0.05 * LIBRARIES + -0.04 * GALERIES + 0.03 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + -0.06 * NUMBER OF EXHIBITS + -0.09 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + -0.04 * MAJOR SPORTS EVENTS PER YEAR + 0.10 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.15 * ARTWORK SITES + 0.19 * POPULAR ARCHITECTURE SITES + 0.08 * HISTORICAL SITES + -0.04 * LOCAL MARKETS + 0.23 * CULTURAL SITES LEVEL III (INTERNATIONAL) + 0.18 * CULTURA SITES LEVEL II (NATIONAL) + -0.05 * CULTURAL SITES LEVEL I (LOCAL) + 0.18 * HERITAGE ARCHITECTURAL HOUSES + -0.01 * % OF LAND THAT CORRESPONDS TO FORESTS + 0.10 * NATIONAL PROTECTED SITES (%) + -0.02 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + 0.03 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + 0.06 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.09 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + 0.15 * NUMBER OF BEACHES AND BEACH RESORTS + 0.18 * LAND AFFECTED BY WILDFIRES + 0.13 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + 0.14 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.07 * RIVERS, LAKES AND WATERFALLS + -0.04 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.10 * GEISERS AND THERMAL CENTERS + 0.22 * PIERS AND SEASHORES + 0.03 * GLACIERS AND WINTER VACATION LOCATIONS + 0.19 * VALLEYS + -0.03 * DESERTS AND DUNES + 0.11 * ISLANDS AND PENINSULAS + -0.12 * PALEONTOLOGY SITES + 0.00 * HIKING TRAILS + 0.10 * PRESERVED SITES + 0.26 * SEASHORE PROTECTED SITES + 0.21 * BIOSHPERE RESERVES + 0.06 * % AVAILABLE WORKFORCE + 0.07 * % POPULATION ORIENTED TOWARDS TOURISM + 0.11 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.03 * 5 POPULATION WITH PRIMARY EDUCATION + 0.07 * % POPULATION WITH SECONDARY EDUCATION + 0.09 * AVERAGE NUMBER OF YEARS STUDYING + 0.10 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + 0.05 * TOURISM-ORIENTED INSTITUTIONS + -0.02 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + -0.03 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + 0.12 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + -0.04 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + -0.04 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + 0.16 * ROOMS PER 1000 HABITANTS + 0.13 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + 0.16 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + -0.03 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + -0.01 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + 0.11 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + -0.02 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + 0.15 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + 0.11 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + 0.07 * NATIONAL TOURISTS ARRIVALS + -0.01 * INTERNATIONAL TOURISTS ARRIVALS + 0.03 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + 0.11 * DENSITY OF AIRPORTS + 0.01 * DENSITY OF ROADS AND HIGHWAYS + -0.06 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + -0.05 * NUMBER OF VEHICLES + 0.16 * VISITORS TO PROTECTED SITES + 0.27 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + 0.07 * TOURIST'S ARRIVALS THROUGH BORDER LINES + -0.05 * SECONDARY ROADS (KMS) + 0.09 * NUMBER OF INTERNATIONAL BORDER GATES 11.1%: -0.06 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + 0.06 * NUMBER OF CULTURAL CENTERS + -0.02 * WORLD CULTURAL HERITAGE SITES + -0.16 * NUMBER OF ARCHEOLOGICAL SITES + 0.03 * NATIONAL MONUMENTS + -0.00 * MUSEUMS + -0.12 * % OF POPULATION THAT ATTENDS MUSEUMS + 0.05 * THEATERS + 0.04 * NUMBER OF THEATER PLAYS PER YEAR + 0.09 * LIBRARIES + 0.04 * GALERIES + 0.21 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + 0.03 * NUMBER OF EXHIBITS + 0.02 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + 0.10 * MAJOR SPORTS EVENTS PER YEAR + -0.14 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + 0.05 * ARTWORK SITES + -0.09 * POPULAR ARCHITECTURE SITES + -0.10 * HISTORICAL SITES + 0.19 * LOCAL MARKETS + -0.07 * CULTURAL SITES LEVEL III (INTERNATIONAL) + 0.00 * CULTURA SITES LEVEL II (NATIONAL) + 0.06 * CULTURAL SITES LEVEL I (LOCAL) + -0.05 * HERITAGE ARCHITECTURAL HOUSES + 0.23 * % OF LAND THAT CORRESPONDS TO FORESTS + 0.12 * NATIONAL PROTECTED SITES (%) + 0.03 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + -0.06 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + 0.04 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.13 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + -0.08 * NUMBER OF BEACHES AND BEACH RESORTS + -0.14 * LAND AFFECTED BY WILDFIRES + 0.19 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + 0.18 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + 0.24 * RIVERS, LAKES AND WATERFALLS + 0.13 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + 0.21 * GEISERS AND THERMAL CENTERS + -0.09 * PIERS AND SEASHORES + 0.14 * GLACIERS AND WINTER VACATION LOCATIONS + -0.04 * VALLEYS + -0.20 * DESERTS AND DUNES + 0.16 * ISLANDS AND PENINSULAS + -0.04 * PALEONTOLOGY SITES + 0.11 * HIKING TRAILS + 0.22 * PRESERVED SITES + 0.04 * SEASHORE PROTECTED SITES + 0.13 * BIOSHPERE RESERVES + -0.03 * % AVAILABLE WORKFORCE + 0.20 * % POPULATION ORIENTED TOWARDS TOURISM + -0.07 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + 0.17 * 5 POPULATION WITH PRIMARY EDUCATION + -0.03 * % POPULATION WITH SECONDARY EDUCATION + -0.17 * AVERAGE NUMBER OF YEARS STUDYING + -0.03 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + 0.05 * TOURISM-ORIENTED INSTITUTIONS + 0.03 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + 0.03 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + 0.06 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + 0.05 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + -0.12 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + 0.05 * ROOMS PER 1000 HABITANTS + 0.10 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + -0.03 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + -0.02 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + -0.05 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + 0.23 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + 0.03 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + -0.03 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + -0.08 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + -0.04 * NATIONAL TOURISTS ARRIVALS + 0.05 * INTERNATIONAL TOURISTS ARRIVALS + 0.04 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + 0.11 * DENSITY OF AIRPORTS + -0.05 * DENSITY OF ROADS AND HIGHWAYS + 0.04 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + 0.03 * NUMBER OF VEHICLES + 0.18 * VISITORS TO PROTECTED SITES + 0.10 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + 0.03 * TOURIST'S ARRIVALS THROUGH BORDER LINES + 0.17 * SECONDARY ROADS (KMS) + 0.10 * NUMBER OF INTERNATIONAL BORDER GATES 7.7%: -0.13 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + 0.05 * NUMBER OF CULTURAL CENTERS + 0.06 * WORLD CULTURAL HERITAGE SITES + -0.06 * NUMBER OF ARCHEOLOGICAL SITES + -0.06 * NATIONAL MONUMENTS + 0.08 * MUSEUMS + -0.03 * % OF POPULATION THAT ATTENDS MUSEUMS + -0.03 * THEATERS + -0.04 * NUMBER OF THEATER PLAYS PER YEAR + -0.05 * LIBRARIES + -0.05 * GALERIES + -0.10 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + -0.02 * NUMBER OF EXHIBITS + -0.12 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + -0.15 * MAJOR SPORTS EVENTS PER YEAR + 0.00 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.16 * ARTWORK SITES + 0.07 * POPULAR ARCHITECTURE SITES + 0.02 * HISTORICAL SITES + -0.09 * LOCAL MARKETS + -0.09 * CULTURAL SITES LEVEL III (INTERNATIONAL) + 0.01 * CULTURA SITES LEVEL II (NATIONAL) + 0.02 * CULTURAL SITES LEVEL I (LOCAL) + -0.05 * HERITAGE ARCHITECTURAL HOUSES + 0.13 * % OF LAND THAT CORRESPONDS TO FORESTS + -0.17 * NATIONAL PROTECTED SITES (%) + -0.07 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + -0.05 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + -0.14 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + 0.07 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + 0.08 * NUMBER OF BEACHES AND BEACH RESORTS + -0.06 * LAND AFFECTED BY WILDFIRES + -0.19 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.16 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.09 * RIVERS, LAKES AND WATERFALLS + -0.01 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + 0.02 * GEISERS AND THERMAL CENTERS + 0.06 * PIERS AND SEASHORES + -0.22 * GLACIERS AND WINTER VACATION LOCATIONS + -0.12 * VALLEYS + 0.10 * DESERTS AND DUNES + 0.29 * ISLANDS AND PENINSULAS + 0.07 * PALEONTOLOGY SITES + -0.19 * HIKING TRAILS + 0.20 * PRESERVED SITES + 0.14 * SEASHORE PROTECTED SITES + -0.12 * BIOSHPERE RESERVES + 0.05 * % AVAILABLE WORKFORCE + 0.24 * % POPULATION ORIENTED TOWARDS TOURISM + -0.10 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.20 * 5 POPULATION WITH PRIMARY EDUCATION + -0.21 * % POPULATION WITH SECONDARY EDUCATION + -0.09 * AVERAGE NUMBER OF YEARS STUDYING + -0.11 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + -0.01 * TOURISM-ORIENTED INSTITUTIONS + -0.03 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + -0.04 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + -0.17 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + -0.02 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + 0.02 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + -0.05 * ROOMS PER 1000 HABITANTS + 0.07 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + -0.09 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + 0.03 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + -0.12 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + 0.14 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + -0.04 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + -0.06 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + 0.02 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + 0.13 * NATIONAL TOURISTS ARRIVALS + -0.04 * INTERNATIONAL TOURISTS ARRIVALS + -0.14 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + 0.22 * DENSITY OF AIRPORTS + 0.00 * DENSITY OF ROADS AND HIGHWAYS + 0.02 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + -0.03 * NUMBER OF VEHICLES + 0.12 * VISITORS TO PROTECTED SITES + 0.04 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + -0.09 * TOURIST'S ARRIVALS THROUGH BORDER LINES + 0.21 * SECONDARY ROADS (KMS) + -0.07 * NUMBER OF INTERNATIONAL BORDER GATES 5.0%: 0.17 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + -0.05 * NUMBER OF CULTURAL CENTERS + 0.11 * WORLD CULTURAL HERITAGE SITES + 0.25 * NUMBER OF ARCHEOLOGICAL SITES + 0.00 * NATIONAL MONUMENTS + -0.08 * MUSEUMS + 0.01 * % OF POPULATION THAT ATTENDS MUSEUMS + -0.01 * THEATERS + 0.01 * NUMBER OF THEATER PLAYS PER YEAR + -0.06 * LIBRARIES + -0.02 * GALERIES + 0.16 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + -0.04 * NUMBER OF EXHIBITS + 0.00 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + 0.02 * MAJOR SPORTS EVENTS PER YEAR + -0.16 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + 0.04 * ARTWORK SITES + 0.01 * POPULAR ARCHITECTURE SITES + -0.16 * HISTORICAL SITES + 0.12 * LOCAL MARKETS + 0.08 * CULTURAL SITES LEVEL III (INTERNATIONAL) + 0.10 * CULTURA SITES LEVEL II (NATIONAL) + -0.09 * CULTURAL SITES LEVEL I (LOCAL) + -0.01 * HERITAGE ARCHITECTURAL HOUSES + 0.02 * % OF LAND THAT CORRESPONDS TO FORESTS + -0.05 * NATIONAL PROTECTED SITES (%) + 0.01 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + 0.29 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + -0.01 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.02 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + -0.09 * NUMBER OF BEACHES AND BEACH RESORTS + 0.04 * LAND AFFECTED BY WILDFIRES + -0.02 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.14 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.15 * RIVERS, LAKES AND WATERFALLS + -0.02 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + 0.16 * GEISERS AND THERMAL CENTERS + -0.24 * PIERS AND SEASHORES + -0.21 * GLACIERS AND WINTER VACATION LOCATIONS + -0.05 * VALLEYS + 0.06 * DESERTS AND DUNES + 0.01 * ISLANDS AND PENINSULAS + -0.23 * PALEONTOLOGY SITES + 0.02 * HIKING TRAILS + 0.06 * PRESERVED SITES + -0.05 * SEASHORE PROTECTED SITES + -0.03 * BIOSHPERE RESERVES + -0.11 * % AVAILABLE WORKFORCE + 0.09 * % POPULATION ORIENTED TOWARDS TOURISM + -0.18 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + 0.01 * 5 POPULATION WITH PRIMARY EDUCATION + 0.16 * % POPULATION WITH SECONDARY EDUCATION + 0.16 * AVERAGE NUMBER OF YEARS STUDYING + -0.01 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + -0.09 * TOURISM-ORIENTED INSTITUTIONS + -0.04 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + -0.04 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + -0.13 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + 0.01 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + 0.18 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + -0.06 * ROOMS PER 1000 HABITANTS + -0.07 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + 0.04 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + 0.04 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + 0.03 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + 0.11 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + 0.00 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + -0.05 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + -0.16 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + -0.08 * NATIONAL TOURISTS ARRIVALS + 0.00 * INTERNATIONAL TOURISTS ARRIVALS + 0.29 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + 0.28 * DENSITY OF AIRPORTS + -0.03 * DENSITY OF ROADS AND HIGHWAYS + -0.14 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + -0.02 * NUMBER OF VEHICLES + -0.06 * VISITORS TO PROTECTED SITES + 0.12 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + 0.07 * TOURIST'S ARRIVALS THROUGH BORDER LINES + -0.06 * SECONDARY ROADS (KMS) + -0.06 * NUMBER OF INTERNATIONAL BORDER GATES 4.9%: 0.12 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + -0.07 * NUMBER OF CULTURAL CENTERS + -0.08 * WORLD CULTURAL HERITAGE SITES + -0.19 * NUMBER OF ARCHEOLOGICAL SITES + -0.01 * NATIONAL MONUMENTS + -0.08 * MUSEUMS + -0.12 * % OF POPULATION THAT ATTENDS MUSEUMS + 0.05 * THEATERS + 0.07 * NUMBER OF THEATER PLAYS PER YEAR + -0.05 * LIBRARIES + 0.00 * GALERIES + -0.02 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + 0.02 * NUMBER OF EXHIBITS + -0.06 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + -0.15 * MAJOR SPORTS EVENTS PER YEAR + 0.18 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.07 * ARTWORK SITES + 0.22 * POPULAR ARCHITECTURE SITES + -0.36 * HISTORICAL SITES + -0.13 * LOCAL MARKETS + -0.15 * CULTURAL SITES LEVEL III (INTERNATIONAL) + 0.01 * CULTURA SITES LEVEL II (NATIONAL) + -0.01 * CULTURAL SITES LEVEL I (LOCAL) + -0.09 * HERITAGE ARCHITECTURAL HOUSES + -0.04 * % OF LAND THAT CORRESPONDS TO FORESTS + 0.07 * NATIONAL PROTECTED SITES (%) + 0.05 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + -0.04 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + -0.08 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.09 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + -0.13 * NUMBER OF BEACHES AND BEACH RESORTS + -0.11 * LAND AFFECTED BY WILDFIRES + -0.06 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.09 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.12 * RIVERS, LAKES AND WATERFALLS + -0.19 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.07 * GEISERS AND THERMAL CENTERS + 0.05 * PIERS AND SEASHORES + 0.06 * GLACIERS AND WINTER VACATION LOCATIONS + 0.07 * VALLEYS + -0.21 * DESERTS AND DUNES + 0.12 * ISLANDS AND PENINSULAS + -0.24 * PALEONTOLOGY SITES + -0.18 * HIKING TRAILS + 0.11 * PRESERVED SITES + 0.07 * SEASHORE PROTECTED SITES + 0.10 * BIOSHPERE RESERVES + 0.08 * % AVAILABLE WORKFORCE + 0.05 * % POPULATION ORIENTED TOWARDS TOURISM + -0.01 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + 0.14 * 5 POPULATION WITH PRIMARY EDUCATION + 0.00 * % POPULATION WITH SECONDARY EDUCATION + -0.07 * AVERAGE NUMBER OF YEARS STUDYING + 0.12 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + -0.06 * TOURISM-ORIENTED INSTITUTIONS + 0.01 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + 0.03 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + 0.04 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + 0.07 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + -0.02 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + 0.06 * ROOMS PER 1000 HABITANTS + -0.05 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + -0.09 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + -0.07 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + 0.20 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + -0.03 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + 0.06 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + 0.05 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + 0.29 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + -0.22 * NATIONAL TOURISTS ARRIVALS + 0.05 * INTERNATIONAL TOURISTS ARRIVALS + 0.09 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + 0.03 * DENSITY OF AIRPORTS + 0.02 * DENSITY OF ROADS AND HIGHWAYS + 0.03 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + 0.03 * NUMBER OF VEHICLES + -0.16 * VISITORS TO PROTECTED SITES + -0.05 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + 0.02 * TOURIST'S ARRIVALS THROUGH BORDER LINES + -0.06 * SECONDARY ROADS (KMS) + -0.14 * NUMBER OF INTERNATIONAL BORDER GATES Total variance explained by the 7 components 84.0%
In [40]:
# Calculate factor scores
pca_model = myPCA.fit_transform(chile_data_s_1)
PCcomponents = pd.DataFrame(data = pca_model, columns = ['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7'])
print("\n The Factor scores are")
PCcomponents
The Factor scores are
Out[40]:
| PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | |
|---|---|---|---|---|---|---|---|
| 0 | -2.386142 | 1.128396 | 0.417084 | -1.039372 | -2.383579 | 5.560673 | 0.896446 |
| 1 | -1.603717 | 2.205502 | 0.000489 | -3.106017 | 1.359288 | 2.146064 | -0.009668 |
| 2 | -1.163348 | 3.041472 | 0.920221 | -3.063266 | 1.643872 | -1.248495 | -3.558966 |
| 3 | -3.022894 | 1.686132 | -1.287520 | -3.948946 | 1.601311 | 0.192783 | -1.124276 |
| 4 | 0.071800 | -3.459654 | 1.681821 | -3.193220 | 1.184838 | -2.570795 | 4.848208 |
| 5 | 5.666810 | -5.185133 | 7.767526 | -1.476143 | -1.618477 | 0.063266 | -2.100251 |
| 6 | 16.071582 | 4.590960 | -2.013523 | 1.269896 | -1.218331 | 0.133082 | 1.318211 |
| 7 | -0.533601 | -4.130965 | -2.604111 | -1.666633 | -1.291375 | 0.430387 | 0.879329 |
| 8 | -0.906211 | -3.216091 | -3.517245 | 0.026947 | -0.489855 | 0.118975 | 0.163107 |
| 9 | 2.200100 | -0.453991 | -3.581660 | -0.554127 | 2.180578 | -2.483251 | -2.047424 |
| 10 | -1.152331 | -4.064711 | -2.573562 | 5.555666 | -2.773524 | -0.040094 | -2.429809 |
| 11 | -2.270740 | -0.234031 | -2.562392 | 0.799648 | 1.506049 | 0.456833 | 0.681446 |
| 12 | -0.571975 | 0.292343 | 3.611886 | 6.352109 | 6.280926 | 1.319659 | 0.674149 |
| 13 | -6.015841 | 2.613051 | 0.379445 | 2.969578 | -2.191594 | -1.956671 | 1.752658 |
| 14 | -4.383492 | 5.186719 | 3.361541 | 1.073879 | -3.790125 | -2.122415 | 0.056839 |
In [41]:
#visualize an example how variables can contribute to diffent principal components
# Fit the model
myPCA = PCA(n_components = 7)
pca_model = myPCA.fit(chile_data_s_1)
y_axis = [0,0,0,0,0,0,0]
for i in range(0,7):
y_axis[i]=[np.mean(pca_model.components_[i][0:24]), np.mean(pca_model.components_[i][24:47]),
np.mean(pca_model.components_[i][47:59]), np.mean(pca_model.components_[i][59:69]),
np.mean(pca_model.components_[i][69:81])]
# Plot
x_axis = ['CULTURAL HERITAGE', 'NATURAL RESOURCES', 'WORKFORCE DEVELOPMENT', 'TOURISM INFRASTRUCTURE', 'TOURISM MOBILITY']
plt.plot(x_axis,y_axis[0], color = 'mediumaquamarine', label = "C1")
plt.plot(x_axis,y_axis[1], color = 'yellow', label = "C2")
plt.plot(x_axis,y_axis[2], color = 'pink', label = "C3")
plt.plot(x_axis,y_axis[3], color = 'steelblue', label = "C4")
plt.plot(x_axis,y_axis[4], color = 'salmon', label = "C5")
plt.plot(x_axis,y_axis[5], color = 'red', label = "C6")
plt.plot(x_axis,y_axis[6], color = 'orange', label = "C7")
plt.xticks(rotation = 90)
plt.title('Example of variable contributions to each principal component')
plt.legend()
pass
4. Developing a scoring system for 5 dimensions
Methodology steps:
Step 1 - Calculate a weighted average for each variable in principal components.
Multiply the percentage value of the explained variance by the percentage value of a feature in the selected principal component. As a result, a weighted average will be a new column in the dataframe with principal components.
In [42]:
# Creating a dataframe of weights
weights = pd.DataFrame(np.column_stack((chile_data_s_1.columns, pca_model.components_[0] *
pca_model.explained_variance_ratio_[0],
pca_model.components_[1] * pca_model.explained_variance_ratio_[1],
pca_model.components_[2] * pca_model.explained_variance_ratio_[2],
pca_model.components_[3] * pca_model.explained_variance_ratio_[3],
pca_model.components_[4] * pca_model.explained_variance_ratio_[4],
pca_model.components_[5] * pca_model.explained_variance_ratio_[5],
pca_model.components_[6] * pca_model.explained_variance_ratio_[6])))
weights = weights.set_index(0)
# Create a weighted average
weights['weighted_average'] = weights.sum(axis = 1)/np.sum(pca_model.explained_variance_ratio_)
# Print
weights.head()
Out[42]:
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | weighted_average | |
|---|---|---|---|---|---|---|---|---|
| 0 | ||||||||
| CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR | -0.0010135 | -0.026766 | -0.00772552 | -0.00715039 | -0.0100109 | 0.00849465 | 0.00587723 | -0.045576 |
| NUMBER OF CULTURAL CENTERS | 0.0571644 | -0.00259764 | 0.000907509 | 0.00659591 | 0.0036928 | -0.00260661 | -0.00364229 | 0.070830 |
| WORLD CULTURAL HERITAGE SITES | 0.0119041 | -0.0165681 | 0.0228346 | -0.0018949 | 0.0042953 | 0.00555241 | -0.003667 | 0.026726 |
| NUMBER OF ARCHEOLOGICAL SITES | -0.00224659 | 9.958e-05 | 0.0151014 | -0.0179738 | -0.00486953 | 0.0123346 | -0.00910945 | -0.007931 |
| NATIONAL MONUMENTS | 0.0588106 | 0.0068198 | 0.000424673 | 0.00349642 | -0.00427658 | 3.51305e-05 | -0.000404655 | 0.077246 |
Step 2. Calculate a score for each dimension.
Multiply weighted average of a variable by each standartized value in each column and sum up results, receiving a final score.
In [43]:
# Example
chile_data_s_1.head(1)
Out[43]:
| CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR | NUMBER OF CULTURAL CENTERS | WORLD CULTURAL HERITAGE SITES | NUMBER OF ARCHEOLOGICAL SITES | NATIONAL MONUMENTS | MUSEUMS | % OF POPULATION THAT ATTENDS MUSEUMS | THEATERS | NUMBER OF THEATER PLAYS PER YEAR | LIBRARIES | GALERIES | % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP | NUMBER OF EXHIBITS | ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR | MAJOR SPORTS EVENTS PER YEAR | OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS | ARTWORK SITES | POPULAR ARCHITECTURE SITES | HISTORICAL SITES | LOCAL MARKETS | CULTURAL SITES LEVEL III (INTERNATIONAL) | CULTURA SITES LEVEL II (NATIONAL) | CULTURAL SITES LEVEL I (LOCAL) | HERITAGE ARCHITECTURAL HOUSES | % OF LAND THAT CORRESPONDS TO FORESTS | NATIONAL PROTECTED SITES (%) | % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS | TOXIC WASTE DISPOSAL (TONS/100 hab.) | NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED | ENVIRONMENTAL ISSUES PER MILLION HABITANTS | NUMBER OF BEACHES AND BEACH RESORTS | LAND AFFECTED BY WILDFIRES | NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) | NATURAL PROTECTED SITES LEVEL II (NATIONAL) | RIVERS, LAKES AND WATERFALLS | MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS | GEISERS AND THERMAL CENTERS | PIERS AND SEASHORES | GLACIERS AND WINTER VACATION LOCATIONS | VALLEYS | DESERTS AND DUNES | ISLANDS AND PENINSULAS | PALEONTOLOGY SITES | HIKING TRAILS | PRESERVED SITES | SEASHORE PROTECTED SITES | BIOSHPERE RESERVES | % AVAILABLE WORKFORCE | % POPULATION ORIENTED TOWARDS TOURISM | AVERAGE MONTHLY INCOME (CHILEAN PESOS) | 5 POPULATION WITH PRIMARY EDUCATION | % POPULATION WITH SECONDARY EDUCATION | AVERAGE NUMBER OF YEARS STUDYING | HIGHER EDUCATION AND TECHNICAL INSTITUTIONS | TOURISM-ORIENTED INSTITUTIONS | NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS | AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS | DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) | CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS | % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR | ROOMS PER 1000 HABITANTS | NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. | TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) | AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR | AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND | NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION | NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS | TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS | TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) | NATIONAL TOURISTS ARRIVALS | INTERNATIONAL TOURISTS ARRIVALS | NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY | DENSITY OF AIRPORTS | DENSITY OF ROADS AND HIGHWAYS | % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) | NUMBER OF VEHICLES | VISITORS TO PROTECTED SITES | NUMBER OF CRUISES THAT ARRIVE PER YEAR | TOURIST'S ARRIVALS THROUGH BORDER LINES | SECONDARY ROADS (KMS) | NUMBER OF INTERNATIONAL BORDER GATES | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Arica y Parinacota | 1.672984 | -0.959349 | -0.559017 | 2.121142 | -0.617597 | -0.721316 | 0.440365 | -0.475143 | -0.335687 | -0.724558 | -0.261024 | 1.389477 | -0.368577 | -0.668153 | -0.454859 | -0.6799 | 0.067176 | 0.326242 | -0.61667 | 0.294619 | 0.360486 | -0.136165 | -0.834058 | -0.355859 | 0.0 | 0.643982 | -0.248227 | 1.426648 | -0.576151 | -0.41055 | -0.836955 | 0.0 | 0.597479 | -0.772105 | -0.685892 | 0.015686 | 0.100452 | -0.947748 | -0.75561 | -0.011653 | -0.118729 | -0.580615 | -0.855528 | -0.386889 | 1.657385e-15 | -0.620174 | 0.476731 | -1.851488 | -0.362785 | -0.632189 | 0.64653 | 2.619732 | 1.554178 | 1.133129 | -0.884585 | -0.47762 | -0.477498 | -0.135893 | -0.524853 | 0.790598 | 0.091521 | -1.132711 | 0.620125 | 0.16632 | 0.816497 | -0.347974 | -0.437516 | -0.521005 | -0.482777 | -0.920689 | -0.322261 | 2.514051 | 1.231771 | -0.062345 | -0.946158 | -0.519001 | -0.762949 | 0.6211 | -0.126593 | -0.862524 | 0.528271 |
As a result, we multiply:
weighted averageforCULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR(-0.045576) in the dataframeweightsby each value in theCULTURAL EVENTS SCHEDULED THROUGHOUT THE YEARcolumn in the dataframechile_data_s_1.Do the same for all weighted averages and columns in respective dataframe
Sum up the product of multiplications and receive the score for the first dimension
In [44]:
# Ranking for dimension 1: CULTURAL HERITAGE AND EVENTS
# Create a dataframe for relevant variables
dim1 = chile_data_s_1.iloc[:, 0:24].mul(weights['weighted_average'][0:24], axis = 1)
# Create a score ranking
dim1['Ranking 1'] = dim1.sum(axis = 1)
# Sort by score
dim1.sort_values(by = 'Ranking 1', ascending = False).head()
Out[44]:
| CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR | NUMBER OF CULTURAL CENTERS | WORLD CULTURAL HERITAGE SITES | NUMBER OF ARCHEOLOGICAL SITES | NATIONAL MONUMENTS | MUSEUMS | % OF POPULATION THAT ATTENDS MUSEUMS | THEATERS | NUMBER OF THEATER PLAYS PER YEAR | LIBRARIES | GALERIES | % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP | NUMBER OF EXHIBITS | ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR | MAJOR SPORTS EVENTS PER YEAR | OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS | ARTWORK SITES | POPULAR ARCHITECTURE SITES | HISTORICAL SITES | LOCAL MARKETS | CULTURAL SITES LEVEL III (INTERNATIONAL) | CULTURA SITES LEVEL II (NATIONAL) | CULTURAL SITES LEVEL I (LOCAL) | HERITAGE ARCHITECTURAL HOUSES | Ranking 1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | |||||||||||||||||||||||||
| Metropolitana | 0.028078 | 0.192903 | -0.014940 | 0.005556 | 0.269217 | 0.214340 | 0.018570 | 0.266501 | 0.287588 | 0.180319 | 0.259461 | -0.008320 | 0.269909 | 0.03980 | 0.018823 | 0.008948 | 0.019085 | -0.001139 | 0.000772 | 0.009099 | -0.001584 | 0.180688 | 0.233427 | 0.028090 | 2.505190 |
| Valparaíso | -0.010561 | 0.101121 | 0.074702 | -0.012192 | 0.064396 | 0.017672 | 0.042596 | -0.012705 | -0.015399 | 0.042557 | 0.041223 | -0.009270 | -0.007536 | -0.03184 | -0.028235 | 0.014540 | 0.019085 | 0.001101 | -0.008496 | 0.009099 | 0.069700 | 0.198829 | 0.021390 | 0.110586 | 0.692363 |
| Biobío | 0.024214 | 0.076968 | -0.014940 | 0.006328 | -0.021578 | 0.086078 | -0.008459 | 0.009455 | -0.007333 | 0.056140 | 0.041223 | -0.008531 | 0.064394 | 0.03980 | 0.018823 | -0.007829 | -0.003817 | -0.000722 | -0.010041 | -0.001400 | -0.008373 | -0.091432 | 0.071609 | 0.002380 | 0.312954 |
| Los Lagos | 0.039669 | 0.038323 | 0.029881 | 0.006328 | -0.017363 | 0.009121 | -0.001702 | -0.008273 | -0.017828 | -0.005950 | -0.031523 | 0.009313 | -0.017811 | 0.03980 | 0.018823 | -0.007829 | 0.041987 | 0.000580 | 0.005406 | 0.003850 | -0.008373 | 0.079096 | 0.004650 | -0.005204 | 0.204969 |
| Antofagasta | 0.039669 | -0.034136 | -0.014940 | -0.015279 | -0.003034 | 0.051875 | 0.055359 | -0.008273 | -0.021181 | -0.036995 | -0.019399 | -0.006631 | -0.017811 | 0.01592 | 0.018823 | 0.008948 | 0.030536 | -0.000149 | -0.020854 | -0.011899 | -0.001584 | -0.015239 | -0.006510 | -0.010938 | -0.023722 |
In [45]:
# Ranking for dimension 2: NATURAL RESOURCES AND SUSTAINABILITY
# Create a dataframe for relevant variables
dim2 = chile_data_s_1.iloc[:, 24:47].mul(weights['weighted_average'][24:47], axis = 1)
# Create a score ranking
dim2['Ranking 2'] = dim2.sum(axis = 1)
# Sort the by score
dim2.sort_values(by = 'Ranking 2', ascending = False).head()
Out[45]:
| % OF LAND THAT CORRESPONDS TO FORESTS | NATIONAL PROTECTED SITES (%) | % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS | TOXIC WASTE DISPOSAL (TONS/100 hab.) | NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED | ENVIRONMENTAL ISSUES PER MILLION HABITANTS | NUMBER OF BEACHES AND BEACH RESORTS | LAND AFFECTED BY WILDFIRES | NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) | NATURAL PROTECTED SITES LEVEL II (NATIONAL) | RIVERS, LAKES AND WATERFALLS | MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS | GEISERS AND THERMAL CENTERS | PIERS AND SEASHORES | GLACIERS AND WINTER VACATION LOCATIONS | VALLEYS | DESERTS AND DUNES | ISLANDS AND PENINSULAS | PALEONTOLOGY SITES | HIKING TRAILS | PRESERVED SITES | SEASHORE PROTECTED SITES | BIOSHPERE RESERVES | Ranking 2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | ||||||||||||||||||||||||
| Los Lagos | 0.039944 | 0.001468 | -0.030505 | -0.013954 | -0.029854 | 0.01565 | -0.000457 | 0.008902 | -0.000437 | -0.004235 | -0.010288 | -0.015863 | 0.000245 | -0.001953 | 0.003823 | -0.004355 | 0.021719 | 0.207479 | 0.043709 | 0.030237 | 2.027671e-01 | 0.106482 | 0.006901 | 0.577425 |
| Metropolitana | -0.016421 | -0.003741 | 0.268483 | 0.015586 | 0.138076 | 0.01565 | 0.020110 | 0.008589 | 0.000766 | 0.019904 | 0.034944 | 0.011442 | -0.000108 | 0.006662 | 0.000348 | 0.001967 | 0.021719 | -0.034927 | 0.043709 | 0.030237 | -2.644788e-02 | -0.030423 | -0.013803 | 0.512320 |
| Valparaíso | -0.016877 | -0.003116 | 0.051114 | -0.005063 | 0.104490 | 0.01565 | -0.047304 | -0.043033 | -0.000570 | -0.023292 | 0.026962 | 0.023145 | -0.000179 | -0.005399 | 0.003823 | 0.029363 | 0.012914 | -0.034927 | 0.043709 | -0.064535 | -2.644788e-02 | 0.106482 | 0.027605 | 0.174514 |
| Coquimbo | -0.023380 | -0.003915 | -0.023316 | -0.036409 | -0.029854 | 0.01565 | -0.022167 | -0.002830 | 0.000633 | 0.011646 | 0.016319 | 0.023145 | -0.000321 | -0.011429 | 0.009037 | 0.020934 | 0.021719 | 0.035449 | 0.019426 | 0.030237 | -2.644788e-02 | 0.060847 | 0.006901 | 0.091873 |
| Arica y Parinacota | 0.000000 | 0.003551 | -0.018664 | 0.042017 | -0.029854 | 0.01565 | 0.014397 | -0.000000 | -0.000570 | 0.013552 | 0.024301 | -0.000260 | 0.000033 | 0.004939 | 0.009037 | -0.000140 | 0.004109 | -0.034927 | 0.043709 | 0.016698 | 1.017919e-16 | -0.030423 | 0.006901 | 0.084054 |
In [46]:
# Ranking for dimension 3: HUMAN RESOURCES AND TOURISM-RELATED WORKFORCE DEVELOPMENT
# Create a dataframe for relevant variables
dim3 = chile_data_s_1.iloc[:, 47:59].mul(weights['weighted_average'][47:59], axis = 1)
# Create a score ranking
dim3['Ranking 3'] = dim3.sum(axis = 1)
# Sort the dataframe by score
dim3.sort_values(by = 'Ranking 3', ascending = False).head()
Out[46]:
| % AVAILABLE WORKFORCE | % POPULATION ORIENTED TOWARDS TOURISM | AVERAGE MONTHLY INCOME (CHILEAN PESOS) | 5 POPULATION WITH PRIMARY EDUCATION | % POPULATION WITH SECONDARY EDUCATION | AVERAGE NUMBER OF YEARS STUDYING | HIGHER EDUCATION AND TECHNICAL INSTITUTIONS | TOURISM-ORIENTED INSTITUTIONS | NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS | AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS | DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) | CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS | Ranking 3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | |||||||||||||
| Metropolitana | 0.008077 | 0.098508 | 0.014620 | 0.000113 | 0.000244 | 0.072541 | 0.024056 | 0.199376 | 0.265943 | 0.278337 | 0.001871 | 0.281558 | 1.245244 |
| Los Lagos | 0.003635 | 0.326682 | -0.011874 | 0.001243 | 0.002256 | -0.053011 | 0.008195 | 0.011285 | -0.020891 | -0.021974 | 0.000349 | 0.008707 | 0.254603 |
| Valparaíso | 0.000266 | -0.031328 | -0.007834 | 0.001243 | -0.001345 | 0.036669 | 0.013482 | 0.114735 | 0.052887 | 0.039654 | 0.001174 | 0.030211 | 0.249814 |
| Biobío | -0.015849 | -0.025182 | -0.012091 | 0.002373 | -0.000074 | -0.005182 | 0.018769 | 0.077117 | 0.054652 | 0.040665 | 0.002176 | 0.001358 | 0.138733 |
| Tarapacá | 0.049863 | -0.019548 | 0.006467 | 0.008022 | 0.002256 | 0.042648 | -0.006344 | -0.035737 | -0.035226 | -0.035360 | 0.000230 | -0.041127 | -0.063857 |
In [47]:
# Ranking for dimension 4: TOURISM INFRASTRUCTURE
# Create a dataframe for relevant variables
dim4 = chile_data_s_1.iloc[:, 59:69].mul(weights['weighted_average'][59:69], axis = 1)
# Create a score ranking
dim4['Ranking 4'] = dim4.sum(axis = 1)
# Sort the dataframe by score
dim4.sort_values(by = 'Ranking 4', ascending = False).head()
Out[47]:
| % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR | ROOMS PER 1000 HABITANTS | NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. | TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) | AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR | AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND | NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION | NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS | TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS | TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) | Ranking 4 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | |||||||||||
| Metropolitana | 0.176745 | -0.005552 | 0.151894 | -0.004538 | 0.156454 | -0.003975 | 0.093013 | 0.301501 | 0.129180 | 0.018534 | 1.013256 |
| Valparaíso | -0.008081 | -0.001808 | 0.157523 | -0.000635 | -0.036338 | 0.001704 | 0.080666 | 0.026308 | 0.164599 | 0.021258 | 0.405196 |
| Los Lagos | -0.035734 | 0.002565 | 0.099583 | 0.000234 | -0.024168 | 0.007383 | 0.241175 | -0.010038 | -0.020377 | -0.005855 | 0.254766 |
| Coquimbo | -0.023189 | -0.000882 | 0.027113 | -0.003656 | -0.001110 | -0.021012 | -0.042802 | -0.020423 | 0.050076 | 0.085215 | 0.049329 |
| Araucanía | -0.061090 | -0.003250 | 0.038719 | -0.002310 | 0.012981 | -0.012494 | 0.093013 | -0.025615 | -0.026807 | -0.008839 | 0.004307 |
In [48]:
# Ranking for dimension 5: TOURISM MOBILITY AND TRANSPORTATION INFRASTRUCTURE
# Create a dataframe for relevant variables
dim5 = chile_data_s_1.iloc[:, 69:81].mul(weights['weighted_average'][69:81], axis = 1)
# Create a score ranking
dim5['Ranking 5'] = dim5.sum(axis = 1)
# Sort the dataframe by score
dim5.sort_values(by = 'Ranking 5', ascending = False).head()
Out[48]:
| NATIONAL TOURISTS ARRIVALS | INTERNATIONAL TOURISTS ARRIVALS | NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY | DENSITY OF AIRPORTS | DENSITY OF ROADS AND HIGHWAYS | % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) | NUMBER OF VEHICLES | VISITORS TO PROTECTED SITES | NUMBER OF CRUISES THAT ARRIVE PER YEAR | TOURIST'S ARRIVALS THROUGH BORDER LINES | SECONDARY ROADS (KMS) | NUMBER OF INTERNATIONAL BORDER GATES | Ranking 5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | |||||||||||||
| Metropolitana | 0.089442 | 0.326733 | 0.168270 | -0.009218 | 0.033727 | 0.105649 | 0.268562 | -0.012999 | -0.034666 | 0.285755 | 0.008728 | 0.001532 | 1.231514 |
| Los Lagos | 0.020777 | 0.005730 | 0.003734 | 0.242869 | 0.007119 | -0.008061 | -0.018203 | 0.126167 | 0.115434 | -0.003540 | 0.074567 | -0.001021 | 0.565572 |
| Valparaíso | 0.082154 | 0.006637 | 0.015188 | -0.025720 | 0.045955 | 0.032697 | 0.027322 | 0.008639 | 0.131516 | 0.070209 | -0.016004 | 0.001532 | 0.380127 |
| Arica y Parinacota | -0.049367 | -0.028702 | 0.180232 | 0.095077 | 0.001714 | -0.041159 | -0.038836 | -0.036609 | 0.035023 | -0.011353 | -0.030269 | -0.001021 | 0.074729 |
| Biobío | 0.067557 | -0.030051 | -0.047279 | -0.042686 | 0.027087 | 0.066302 | 0.026217 | -0.024601 | -0.034666 | -0.061805 | 0.071704 | 0.001532 | 0.019311 |
In [50]:
# Create an aggregated dataframe with all scores
scoring_data = pd.concat([dim1.iloc[:,-1:], dim2.iloc[:,-1:], dim3.iloc[:,-1:], dim4.iloc[:,-1:], dim5.iloc[:,-1:]], axis=1)
scoring_data
Out[50]:
| Ranking 1 | Ranking 2 | Ranking 3 | Ranking 4 | Ranking 5 | |
|---|---|---|---|---|---|
| Region | |||||
| Arica y Parinacota | -0.415952 | 0.084054 | -0.227440 | -0.142092 | 0.074729 |
| Tarapacá | -0.173136 | -0.013629 | -0.063857 | -0.051780 | -0.111462 |
| Antofagasta | -0.023722 | -0.210598 | -0.075573 | 0.003949 | -0.066772 |
| Atacama | -0.315089 | -0.340057 | -0.188900 | -0.203309 | -0.415831 |
| Coquimbo | -0.250916 | 0.091873 | -0.128560 | 0.049329 | -0.217581 |
| Valparaíso | 0.692363 | 0.174514 | 0.249814 | 0.405196 | 0.380127 |
| Metropolitana | 2.505190 | 0.512320 | 1.245244 | 1.013256 | 1.231514 |
| O'Higgins | -0.571647 | -0.122728 | -0.138538 | -0.318736 | -0.295041 |
| Maule | -0.476902 | -0.145830 | -0.227193 | -0.242213 | -0.240416 |
| Biobío | 0.312954 | -0.259354 | 0.138733 | -0.096586 | 0.019311 |
| Araucanía | -0.510915 | -0.330403 | -0.159923 | 0.004307 | -0.067661 |
| Los Ríos | -0.294472 | 0.045124 | -0.245964 | -0.086307 | -0.333723 |
| Los Lagos | 0.204969 | 0.577425 | 0.254603 | 0.254766 | 0.565572 |
| Aysén | -0.441574 | -0.012318 | -0.253732 | -0.422402 | -0.465189 |
| Magallanes y Antártica | -0.241153 | -0.050395 | -0.178714 | -0.167379 | -0.057579 |
In [51]:
scoring_data.style.highlight_null().render().split('\n')[:10]
def color_negative_red(val):
"""
Takes a scalar and returns a string with
the css property `'color: red'` for negative
strings, black otherwise.
"""
color = 'red' if val < 0 else 'black'
return 'color: %s' % color
def highlight_max(s):
'''
highlight the maximum in a Series yellow.
'''
is_max = s == s.max()
return ['background-color: yellow' if v else '' for v in is_max]
scoring_data.style.\
applymap(color_negative_red).\
apply(highlight_max)
Out[51]:
| Ranking 1 | Ranking 2 | Ranking 3 | Ranking 4 | Ranking 5 | |
|---|---|---|---|---|---|
| Region | |||||
| Arica y Parinacota | -0.415952 | 0.084054 | -0.227440 | -0.142092 | 0.074729 |
| Tarapacá | -0.173136 | -0.013629 | -0.063857 | -0.051780 | -0.111462 |
| Antofagasta | -0.023722 | -0.210598 | -0.075573 | 0.003949 | -0.066772 |
| Atacama | -0.315089 | -0.340057 | -0.188900 | -0.203309 | -0.415831 |
| Coquimbo | -0.250916 | 0.091873 | -0.128560 | 0.049329 | -0.217581 |
| Valparaíso | 0.692363 | 0.174514 | 0.249814 | 0.405196 | 0.380127 |
| Metropolitana | 2.505190 | 0.512320 | 1.245244 | 1.013256 | 1.231514 |
| O'Higgins | -0.571647 | -0.122728 | -0.138538 | -0.318736 | -0.295041 |
| Maule | -0.476902 | -0.145830 | -0.227193 | -0.242213 | -0.240416 |
| Biobío | 0.312954 | -0.259354 | 0.138733 | -0.096586 | 0.019311 |
| Araucanía | -0.510915 | -0.330403 | -0.159923 | 0.004307 | -0.067661 |
| Los Ríos | -0.294472 | 0.045124 | -0.245964 | -0.086307 | -0.333723 |
| Los Lagos | 0.204969 | 0.577425 | 0.254603 | 0.254766 | 0.565572 |
| Aysén | -0.441574 | -0.012318 | -0.253732 | -0.422402 | -0.465189 |
| Magallanes y Antártica | -0.241153 | -0.050395 | -0.178714 | -0.167379 | -0.057579 |
Reapeat the above for dimensions 6-10
In [52]:
chile_data_2 = pd.read_csv('Tourism Chile D6 - D10 (English).csv', encoding = 'ISO-8859-1', header = 2)
In [53]:
chile_data_2 = chile_data_2[:-3]
chile_data_2 = chile_data_2[:-1]
In [54]:
chile_data_2
Out[54]:
| Region | Density of restaurants and other food services per 100,000 inhabitants | Density of People employed in restaurants and the like per 10,000 inhabitants | Car rental companies | Densidad de camas en hospitales por cada 10.000 habitantes | Density of beds in hospitals per 10,000 inhabitants | Number of spas | Density of gambling casinos per million inhabitants | Number of golf courses | Number of craft centers | Density of tour guides per 100,000 inhabitants | Number of thermal centers | Density of Sports Facilities and Venues per 10,000 inhabitants | Number of Winter Centers | Number of shopping centers | Penetration of telephone lines in service per 100 inhabitants | Density of service stations | Number of tour-operator companies certified with the tourism quality seal | Number of Vineyards and Wine Routes | Perception of exposure to crime (%) | Percentage of victimized households with at least one victim | Density of homicides per million inhabitants | Density of crimes against public health per million inhabitants | Black figure index | Budget for public safety (Thousands of $) | Percentage of households that reported at least one crime | Number of declared crimes | Number of crimes investigated | Number of accidents (roads, air and waterways) | Illegal commerce | Number of Carabineros | Unemployment rate | Poverty rate | Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants | Number of strikes carried out | Average (days) duration of a strike | Person-day cost of a strike | Density of Bank Branches per million inhabitants | Floating population | Volume of exports | Density of Tourist Information Offices per million inhabitants | Number of visits to Tourist Information Offices | Average monthly global searches by tourist attraction on the internet | National tourism promotion budget (Thousands of USD) | International tourism promotion budget (Thousands of USD) | FNDR resources allocated to promotion (Thousands of USD) | Investments in public infrastructure made by the Ministry of Public Works | Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) | Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population | Funds obtained from FNRD (Thousands of pesos) | Number of regional strategic development plans | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Arica y Parinacota | 35.857 | 19.563 | 12 | 21.619 | 43.766 | 0 | 5.273 | 1 | 20 | 20.038 | 5 | 0 | - | - | 240.55 | 17 | 6 | - | 0.318 | 0.26 | 42.184 | 15.819 | 0.513 | 11,884,613 | 0.426 | 26,467 | 2,387 | 981 | 319 | 265 | 7.3 | 8.2 | 84.369 | 1 | 11 | 253 | 94.915 | 45,560 | 137 | 5.273 | 6,517 | 1,060,490 | 193,364 | 118,413 | 92,641 | 36,370,835 | 11,757,543 | 446,922 | 24,729,573 | 1 |
| 1 | Tarapacá | 25.947 | 26.616 | 2 | 18.958 | 60.264 | 0 | 4.185 | 3 | 9 | 22.18 | 5 | 40.134 | - | 2 | 213.07 | 28 | 0 | - | 0.421 | 0.468 | 87.884 | 0 | 0.516 | 14,740,693 | 0.398 | 76,896 | 1,862 | 1,105 | 593 | 592 | 4.8 | 13.4 | 485.457 | 6 | 14.9 | 59,745 | 138.104 | 83,265 | 3,236 | 4.185 | 7,600 | 1,750,481 | 74,404 | 13,600 | - | 42,364,100 | 12,796,789 | 355,208 | 29,714,382 | 2 |
| 2 | Antofagasta | 37.248 | 35.446 | 22 | 29.96 | 69.233 | 3 | 4.049 | 3 | 36 | 20.446 | 2 | 14.292 | - | 3 | 259.23 | 39 | 2 | - | 0.546 | 0.314 | 109.315 | 0 | 0.381 | 22,383,612 | 0.495 | 123,017 | 4,228 | 2,324 | 7 | 765 | 6.2 | 7.3 | 12.146 | 13 | 9.6 | 30,220 | 178.143 | 238,010 | 16,666 | 4.049 | 16,280 | 2,846,340 | 200,000 | 149,000 | 149,744 | 38,262,875 | 29,066,423 | 192,572 | 50,841,622 | 1 |
| 3 | Atacama | 44.823 | 26.933 | 19 | 25.478 | 56.225 | 1 | 3.932 | 1 | 9 | 18.479 | 3 | 32.437 | - | - | 184.01 | 35 | 1 | - | 0.39 | 0.197 | 66.841 | 0 | 0.488 | 17,068,077 | 0.533 | 21,705 | 3,125 | 965 | 14 | 504 | 6.4 | 10.2 | 15.727 | 3 | 29.3 | 17,860 | 145.477 | 53,046 | 3,456 | 3.932 | 11,614 | 556,668 | 94,100 | 187,035 | - | 35,948,639 | 16,986,593 | 233,225 | 30,224,699 | 2 |
| 4 | Coquimbo | 37.632 | 25.165 | 9 | 17.805 | 47.745 | 3 | 3.316 | 3 | 13 | 6.963 | 3 | 14.091 | - | 1 | 150.06 | 72 | 3 | - | 0.288 | 0.218 | 155.833 | 0 | 0.396 | 20,225,710 | 0.449 | 69,974 | 4,203 | 1,831 | 100 | 1,082 | 6 | 12.3 | 11.605 | 3 | 29 | 1,431 | 112.73 | 275,447 | 2,229 | 1.658 | 11,056 | 5,364,222 | 189,900 | 72,917 | - | 64,917,630 | 33,222,779 | 673,800 | 44,790,979 | 3 |
| 5 | Valparaíso | 52.408 | 47.05 | 16 | 27.756 | 55.85 | 9 | 1.948 | 12 | 44 | 12.209 | 3 | 18.71 | 2 | 8 | 230.65 | 179 | 3 | 5 | 0.311 | 0.291 | 87.671 | 1.948 | 0.432 | 24,137,492 | 0.445 | 236,177 | 19,176 | 6,765 | 166 | 2,761 | 7.5 | 11.6 | 50.654 | 10 | 10.7 | 16,641 | 143.52 | 1,557,887 | 4,287 | 1.299 | 5,649 | 3,058,423 | 129,481 | 63,996 | 1,079,295 | 101,802,434 | 24,239,428 | 811,772 | 51,647,041 | 1 |
| 6 | Metropolitana | 9.223 | 16.914 | 47 | 25.508 | 65.499 | 0 | 0 | 21 | 42 | 4.834 | 4 | 14.717 | 5 | 33 | 331.61 | 507 | 39 | 1 | 0.372 | 0.295 | 65.334 | 0.66 | 0.47 | 60,400,444 | 0.424 | 1,146,510 | 53,517 | 23,242 | 7,605 | 9,385 | 7.7 | 8.8 | 39.596 | 102 | 12.3 | 147,198 | 1295.113 | 7,307,884 | 6,196 | 0.33 | 19,352 | 13,709,951 | 56,982 | - | - | 230,562,951 | 43,552,270 | 62,052 | 90,875,903 | 1 |
| 7 | O'Higgins | 18.959 | 49.422 | 6 | 19.946 | 43.427 | 0 | 2.562 | 3 | 39 | 9.992 | 2 | 14.95 | 1 | 2 | 117.3 | 114 | 3 | 7 | 0.203 | 0.22 | 51.241 | 0 | 0.396 | 18,182,163 | 0.477 | 111,020 | 4,894 | 2,451 | 762 | 1,136 | 6 | 9.9 | 11.529 | 7 | 10.2 | 26,831 | 95.805 | 265,601 | 3,056 | 1.281 | 4,700 | 754,000 | 179,000 | 6,000 | 345,000 | 50,200,910 | 17,850,727 | 444,491 | 43,045,390 | 1 |
| 8 | Maule | 15.637 | 19.943 | 10 | 23.852 | 35.899 | 9 | 1.101 | 1 | 24 | 8.7 | 5 | 25.383 | - | 3 | 109.73 | 130 | 0 | 2 | 0.245 | 0.227 | 27.53 | 1.101 | 0.427 | 25,936,948 | 0.53 | 124,820 | 6,018 | 4,771 | 194 | 1,875 | 6.4 | 15.8 | 11.012 | 5 | 10.7 | 1,371 | 48.346 | 165,417 | 1,311 | 1.101 | 7,183 | 647,510 | 168,850 | 8,000 | 218,000 | 97,730,159 | 19,292,650 | 486,500 | 48,826,193 | 2 |
| 9 | Biobío | 14.128 | 14.004 | 16 | 27.16 | 38.194 | 2 | 1.612 | 6 | 26 | 1.612 | 3 | 20.531 | 1 | 3 | 162.84 | 211 | 1 | - | 0.36 | 0.31 | 63.925 | 0 | 0.42 | 41,208,340 | 0.41 | 292,281 | 19,222 | 5,713 | 37 | 3,193 | 7.9 | 15.8 | 12.892 | 12 | 11.8 | 15,518 | 188.607 | 305,920 | 4,475 | 1.074 | 7,362 | 3,548,528 | 317,000 | 44,000 | 319,660 | 137,998,425 | 28,228,357 | 1,646,127 | 88,172,439 | 3 |
| 10 | Araucanía | 15.986 | 15.687 | 25 | 25.887 | 40.826 | 23 | 2.3 | 2 | 16 | 9.085 | 13 | 22.736 | 4 | 1 | 135.88 | 118 | 5 | - | 0.381 | 0.288 | 78.203 | 1.15 | 0.369 | 29,000,528 | 0.469 | 140,063 | 8,685 | 3,161 | 323 | 1,550 | 8 | 18.1 | 20.701 | 4 | 30.3 | 5,856 | 232.887 | 395,583 | 372 | 1.15 | 9,488 | 1,842,375 | 108,799 | 27,507 | - | 67,212,297 | 31,050,589 | - | 54,529,735 | 2 |
| 11 | Los Ríos | 34.512 | 26.207 | 9 | 30.472 | 42.649 | 2 | 2.806 | 1 | 40 | 8.698 | 12 | 0 | 1 | - | 191.32 | 44 | 1 | - | 0.274 | 0.27 | 101.011 | 0 | 0.298 | 15,834,434 | 0.44 | 22,076 | 3,235 | 818 | 29 | 773 | 6.4 | 14.3 | 11.223 | 4 | 11.3 | 6,171 | 51.623 | 71,200 | 71 | 2.806 | 7,109 | 1,476,220 | 253,292 | 35,000 | 13,000 | 49,376,288 | 22,065,484 | 486,875 | 32,409,855 | 1 |
| 12 | Los Lagos | 36.415 | 27.36 | 24 | 22.644 | 47.298 | 2 | 4.186 | 2 | 27 | 20.928 | 8 | 25.728 | 2 | 2 | 162.58 | 86 | 6 | - | 0.323 | 0.201 | 104.641 | 0 | 0.374 | 19,901,362 | 0.426 | 85,949 | 8,729 | 2,023 | 192 | 1,409 | 3.5 | 11.8 | 23.719 | 2 | 8 | 2,160 | 994.622 | 122,157 | 2,251 | 4.186 | 4,708 | 7,108,761 | 264,511 | 96,915 | 63,500 | 101,170,739 | 40,816,121 | 737,875 | 62,581,190 | 2 |
| 13 | Aysén | 76.509 | 40.987 | 15 | 27.543 | 43.72 | 0 | 10.93 | 0 | 57 | 67.765 | 4 | 24.264 | 1 | - | 170.81 | 20 | 0 | - | 0.351 | 0.256 | 174.879 | 10.93 | - | 15,972,532 | 0.445 | 14,119 | 3,349 | 422 | - | 469 | 4.4 | 5.2 | 32.79 | 1 | 7 | 1,953 | 106.083 | 13,667 | 379 | 10.93 | 5,660 | 200,654 | 65,000 | 454,500 | 536,488 | 32,558,406 | 13,692,259 | 794,614 | 29,462,726 | 2 |
| 14 | Magallanes y Antártica | 59.008 | 59.34 | 12 | 34.676 | 71.606 | 0 | 13.26 | 1 | 15 | 137.244 | 0 | 18.366 | 1 | 2 | 297.25 | 21 | 3 | - | 0.283 | 0.18 | 53.041 | 6.63 | 0.367 | 20,075,842 | 0.414 | 8,659 | 2,527 | 660 | - | 216 | 5.4 | 6.1 | 86.192 | 1 | 22 | 616 | 5.115 | 322,800 | 397 | 13.26 | 4,217 | 558,678 | 266,000 | 525,000 | 32,100 | 30,955,753 | 18,036,736 | 357,752 | 30,288,105 | 1 |
In [55]:
chile_data_2.shape
Out[55]:
(15, 51)
In [56]:
chile_data_2 = chile_data_2.rename(columns={'VARIABLE': 'Region'})
In [57]:
chile_data_2
Out[57]:
| Region | Density of restaurants and other food services per 100,000 inhabitants | Density of People employed in restaurants and the like per 10,000 inhabitants | Car rental companies | Densidad de camas en hospitales por cada 10.000 habitantes | Density of beds in hospitals per 10,000 inhabitants | Number of spas | Density of gambling casinos per million inhabitants | Number of golf courses | Number of craft centers | Density of tour guides per 100,000 inhabitants | Number of thermal centers | Density of Sports Facilities and Venues per 10,000 inhabitants | Number of Winter Centers | Number of shopping centers | Penetration of telephone lines in service per 100 inhabitants | Density of service stations | Number of tour-operator companies certified with the tourism quality seal | Number of Vineyards and Wine Routes | Perception of exposure to crime (%) | Percentage of victimized households with at least one victim | Density of homicides per million inhabitants | Density of crimes against public health per million inhabitants | Black figure index | Budget for public safety (Thousands of $) | Percentage of households that reported at least one crime | Number of declared crimes | Number of crimes investigated | Number of accidents (roads, air and waterways) | Illegal commerce | Number of Carabineros | Unemployment rate | Poverty rate | Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants | Number of strikes carried out | Average (days) duration of a strike | Person-day cost of a strike | Density of Bank Branches per million inhabitants | Floating population | Volume of exports | Density of Tourist Information Offices per million inhabitants | Number of visits to Tourist Information Offices | Average monthly global searches by tourist attraction on the internet | National tourism promotion budget (Thousands of USD) | International tourism promotion budget (Thousands of USD) | FNDR resources allocated to promotion (Thousands of USD) | Investments in public infrastructure made by the Ministry of Public Works | Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) | Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population | Funds obtained from FNRD (Thousands of pesos) | Number of regional strategic development plans | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Arica y Parinacota | 35.857 | 19.563 | 12 | 21.619 | 43.766 | 0 | 5.273 | 1 | 20 | 20.038 | 5 | 0 | - | - | 240.55 | 17 | 6 | - | 0.318 | 0.26 | 42.184 | 15.819 | 0.513 | 11,884,613 | 0.426 | 26,467 | 2,387 | 981 | 319 | 265 | 7.3 | 8.2 | 84.369 | 1 | 11 | 253 | 94.915 | 45,560 | 137 | 5.273 | 6,517 | 1,060,490 | 193,364 | 118,413 | 92,641 | 36,370,835 | 11,757,543 | 446,922 | 24,729,573 | 1 |
| 1 | Tarapacá | 25.947 | 26.616 | 2 | 18.958 | 60.264 | 0 | 4.185 | 3 | 9 | 22.18 | 5 | 40.134 | - | 2 | 213.07 | 28 | 0 | - | 0.421 | 0.468 | 87.884 | 0 | 0.516 | 14,740,693 | 0.398 | 76,896 | 1,862 | 1,105 | 593 | 592 | 4.8 | 13.4 | 485.457 | 6 | 14.9 | 59,745 | 138.104 | 83,265 | 3,236 | 4.185 | 7,600 | 1,750,481 | 74,404 | 13,600 | - | 42,364,100 | 12,796,789 | 355,208 | 29,714,382 | 2 |
| 2 | Antofagasta | 37.248 | 35.446 | 22 | 29.96 | 69.233 | 3 | 4.049 | 3 | 36 | 20.446 | 2 | 14.292 | - | 3 | 259.23 | 39 | 2 | - | 0.546 | 0.314 | 109.315 | 0 | 0.381 | 22,383,612 | 0.495 | 123,017 | 4,228 | 2,324 | 7 | 765 | 6.2 | 7.3 | 12.146 | 13 | 9.6 | 30,220 | 178.143 | 238,010 | 16,666 | 4.049 | 16,280 | 2,846,340 | 200,000 | 149,000 | 149,744 | 38,262,875 | 29,066,423 | 192,572 | 50,841,622 | 1 |
| 3 | Atacama | 44.823 | 26.933 | 19 | 25.478 | 56.225 | 1 | 3.932 | 1 | 9 | 18.479 | 3 | 32.437 | - | - | 184.01 | 35 | 1 | - | 0.39 | 0.197 | 66.841 | 0 | 0.488 | 17,068,077 | 0.533 | 21,705 | 3,125 | 965 | 14 | 504 | 6.4 | 10.2 | 15.727 | 3 | 29.3 | 17,860 | 145.477 | 53,046 | 3,456 | 3.932 | 11,614 | 556,668 | 94,100 | 187,035 | - | 35,948,639 | 16,986,593 | 233,225 | 30,224,699 | 2 |
| 4 | Coquimbo | 37.632 | 25.165 | 9 | 17.805 | 47.745 | 3 | 3.316 | 3 | 13 | 6.963 | 3 | 14.091 | - | 1 | 150.06 | 72 | 3 | - | 0.288 | 0.218 | 155.833 | 0 | 0.396 | 20,225,710 | 0.449 | 69,974 | 4,203 | 1,831 | 100 | 1,082 | 6 | 12.3 | 11.605 | 3 | 29 | 1,431 | 112.73 | 275,447 | 2,229 | 1.658 | 11,056 | 5,364,222 | 189,900 | 72,917 | - | 64,917,630 | 33,222,779 | 673,800 | 44,790,979 | 3 |
| 5 | Valparaíso | 52.408 | 47.05 | 16 | 27.756 | 55.85 | 9 | 1.948 | 12 | 44 | 12.209 | 3 | 18.71 | 2 | 8 | 230.65 | 179 | 3 | 5 | 0.311 | 0.291 | 87.671 | 1.948 | 0.432 | 24,137,492 | 0.445 | 236,177 | 19,176 | 6,765 | 166 | 2,761 | 7.5 | 11.6 | 50.654 | 10 | 10.7 | 16,641 | 143.52 | 1,557,887 | 4,287 | 1.299 | 5,649 | 3,058,423 | 129,481 | 63,996 | 1,079,295 | 101,802,434 | 24,239,428 | 811,772 | 51,647,041 | 1 |
| 6 | Metropolitana | 9.223 | 16.914 | 47 | 25.508 | 65.499 | 0 | 0 | 21 | 42 | 4.834 | 4 | 14.717 | 5 | 33 | 331.61 | 507 | 39 | 1 | 0.372 | 0.295 | 65.334 | 0.66 | 0.47 | 60,400,444 | 0.424 | 1,146,510 | 53,517 | 23,242 | 7,605 | 9,385 | 7.7 | 8.8 | 39.596 | 102 | 12.3 | 147,198 | 1295.113 | 7,307,884 | 6,196 | 0.33 | 19,352 | 13,709,951 | 56,982 | - | - | 230,562,951 | 43,552,270 | 62,052 | 90,875,903 | 1 |
| 7 | O'Higgins | 18.959 | 49.422 | 6 | 19.946 | 43.427 | 0 | 2.562 | 3 | 39 | 9.992 | 2 | 14.95 | 1 | 2 | 117.3 | 114 | 3 | 7 | 0.203 | 0.22 | 51.241 | 0 | 0.396 | 18,182,163 | 0.477 | 111,020 | 4,894 | 2,451 | 762 | 1,136 | 6 | 9.9 | 11.529 | 7 | 10.2 | 26,831 | 95.805 | 265,601 | 3,056 | 1.281 | 4,700 | 754,000 | 179,000 | 6,000 | 345,000 | 50,200,910 | 17,850,727 | 444,491 | 43,045,390 | 1 |
| 8 | Maule | 15.637 | 19.943 | 10 | 23.852 | 35.899 | 9 | 1.101 | 1 | 24 | 8.7 | 5 | 25.383 | - | 3 | 109.73 | 130 | 0 | 2 | 0.245 | 0.227 | 27.53 | 1.101 | 0.427 | 25,936,948 | 0.53 | 124,820 | 6,018 | 4,771 | 194 | 1,875 | 6.4 | 15.8 | 11.012 | 5 | 10.7 | 1,371 | 48.346 | 165,417 | 1,311 | 1.101 | 7,183 | 647,510 | 168,850 | 8,000 | 218,000 | 97,730,159 | 19,292,650 | 486,500 | 48,826,193 | 2 |
| 9 | Biobío | 14.128 | 14.004 | 16 | 27.16 | 38.194 | 2 | 1.612 | 6 | 26 | 1.612 | 3 | 20.531 | 1 | 3 | 162.84 | 211 | 1 | - | 0.36 | 0.31 | 63.925 | 0 | 0.42 | 41,208,340 | 0.41 | 292,281 | 19,222 | 5,713 | 37 | 3,193 | 7.9 | 15.8 | 12.892 | 12 | 11.8 | 15,518 | 188.607 | 305,920 | 4,475 | 1.074 | 7,362 | 3,548,528 | 317,000 | 44,000 | 319,660 | 137,998,425 | 28,228,357 | 1,646,127 | 88,172,439 | 3 |
| 10 | Araucanía | 15.986 | 15.687 | 25 | 25.887 | 40.826 | 23 | 2.3 | 2 | 16 | 9.085 | 13 | 22.736 | 4 | 1 | 135.88 | 118 | 5 | - | 0.381 | 0.288 | 78.203 | 1.15 | 0.369 | 29,000,528 | 0.469 | 140,063 | 8,685 | 3,161 | 323 | 1,550 | 8 | 18.1 | 20.701 | 4 | 30.3 | 5,856 | 232.887 | 395,583 | 372 | 1.15 | 9,488 | 1,842,375 | 108,799 | 27,507 | - | 67,212,297 | 31,050,589 | - | 54,529,735 | 2 |
| 11 | Los Ríos | 34.512 | 26.207 | 9 | 30.472 | 42.649 | 2 | 2.806 | 1 | 40 | 8.698 | 12 | 0 | 1 | - | 191.32 | 44 | 1 | - | 0.274 | 0.27 | 101.011 | 0 | 0.298 | 15,834,434 | 0.44 | 22,076 | 3,235 | 818 | 29 | 773 | 6.4 | 14.3 | 11.223 | 4 | 11.3 | 6,171 | 51.623 | 71,200 | 71 | 2.806 | 7,109 | 1,476,220 | 253,292 | 35,000 | 13,000 | 49,376,288 | 22,065,484 | 486,875 | 32,409,855 | 1 |
| 12 | Los Lagos | 36.415 | 27.36 | 24 | 22.644 | 47.298 | 2 | 4.186 | 2 | 27 | 20.928 | 8 | 25.728 | 2 | 2 | 162.58 | 86 | 6 | - | 0.323 | 0.201 | 104.641 | 0 | 0.374 | 19,901,362 | 0.426 | 85,949 | 8,729 | 2,023 | 192 | 1,409 | 3.5 | 11.8 | 23.719 | 2 | 8 | 2,160 | 994.622 | 122,157 | 2,251 | 4.186 | 4,708 | 7,108,761 | 264,511 | 96,915 | 63,500 | 101,170,739 | 40,816,121 | 737,875 | 62,581,190 | 2 |
| 13 | Aysén | 76.509 | 40.987 | 15 | 27.543 | 43.72 | 0 | 10.93 | 0 | 57 | 67.765 | 4 | 24.264 | 1 | - | 170.81 | 20 | 0 | - | 0.351 | 0.256 | 174.879 | 10.93 | - | 15,972,532 | 0.445 | 14,119 | 3,349 | 422 | - | 469 | 4.4 | 5.2 | 32.79 | 1 | 7 | 1,953 | 106.083 | 13,667 | 379 | 10.93 | 5,660 | 200,654 | 65,000 | 454,500 | 536,488 | 32,558,406 | 13,692,259 | 794,614 | 29,462,726 | 2 |
| 14 | Magallanes y Antártica | 59.008 | 59.34 | 12 | 34.676 | 71.606 | 0 | 13.26 | 1 | 15 | 137.244 | 0 | 18.366 | 1 | 2 | 297.25 | 21 | 3 | - | 0.283 | 0.18 | 53.041 | 6.63 | 0.367 | 20,075,842 | 0.414 | 8,659 | 2,527 | 660 | - | 216 | 5.4 | 6.1 | 86.192 | 1 | 22 | 616 | 5.115 | 322,800 | 397 | 13.26 | 4,217 | 558,678 | 266,000 | 525,000 | 32,100 | 30,955,753 | 18,036,736 | 357,752 | 30,288,105 | 1 |
In [58]:
chile_data_2.loc[:, chile_data_2.dtypes == np.object]
Out[58]:
| Region | Density of restaurants and other food services per 100,000 inhabitants | Density of People employed in restaurants and the like per 10,000 inhabitants | Car rental companies | Densidad de camas en hospitales por cada 10.000 habitantes | Density of beds in hospitals per 10,000 inhabitants | Number of spas | Density of gambling casinos per million inhabitants | Number of golf courses | Number of craft centers | Density of tour guides per 100,000 inhabitants | Number of thermal centers | Density of Sports Facilities and Venues per 10,000 inhabitants | Number of Winter Centers | Number of shopping centers | Penetration of telephone lines in service per 100 inhabitants | Density of service stations | Number of tour-operator companies certified with the tourism quality seal | Number of Vineyards and Wine Routes | Perception of exposure to crime (%) | Percentage of victimized households with at least one victim | Density of homicides per million inhabitants | Density of crimes against public health per million inhabitants | Black figure index | Budget for public safety (Thousands of $) | Percentage of households that reported at least one crime | Number of declared crimes | Number of crimes investigated | Number of accidents (roads, air and waterways) | Illegal commerce | Number of Carabineros | Unemployment rate | Poverty rate | Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants | Number of strikes carried out | Average (days) duration of a strike | Person-day cost of a strike | Density of Bank Branches per million inhabitants | Floating population | Volume of exports | Density of Tourist Information Offices per million inhabitants | Number of visits to Tourist Information Offices | Average monthly global searches by tourist attraction on the internet | National tourism promotion budget (Thousands of USD) | International tourism promotion budget (Thousands of USD) | FNDR resources allocated to promotion (Thousands of USD) | Investments in public infrastructure made by the Ministry of Public Works | Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) | Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population | Funds obtained from FNRD (Thousands of pesos) | Number of regional strategic development plans | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Arica y Parinacota | 35.857 | 19.563 | 12 | 21.619 | 43.766 | 0 | 5.273 | 1 | 20 | 20.038 | 5 | 0 | - | - | 240.55 | 17 | 6 | - | 0.318 | 0.26 | 42.184 | 15.819 | 0.513 | 11,884,613 | 0.426 | 26,467 | 2,387 | 981 | 319 | 265 | 7.3 | 8.2 | 84.369 | 1 | 11 | 253 | 94.915 | 45,560 | 137 | 5.273 | 6,517 | 1,060,490 | 193,364 | 118,413 | 92,641 | 36,370,835 | 11,757,543 | 446,922 | 24,729,573 | 1 |
| 1 | Tarapacá | 25.947 | 26.616 | 2 | 18.958 | 60.264 | 0 | 4.185 | 3 | 9 | 22.18 | 5 | 40.134 | - | 2 | 213.07 | 28 | 0 | - | 0.421 | 0.468 | 87.884 | 0 | 0.516 | 14,740,693 | 0.398 | 76,896 | 1,862 | 1,105 | 593 | 592 | 4.8 | 13.4 | 485.457 | 6 | 14.9 | 59,745 | 138.104 | 83,265 | 3,236 | 4.185 | 7,600 | 1,750,481 | 74,404 | 13,600 | - | 42,364,100 | 12,796,789 | 355,208 | 29,714,382 | 2 |
| 2 | Antofagasta | 37.248 | 35.446 | 22 | 29.96 | 69.233 | 3 | 4.049 | 3 | 36 | 20.446 | 2 | 14.292 | - | 3 | 259.23 | 39 | 2 | - | 0.546 | 0.314 | 109.315 | 0 | 0.381 | 22,383,612 | 0.495 | 123,017 | 4,228 | 2,324 | 7 | 765 | 6.2 | 7.3 | 12.146 | 13 | 9.6 | 30,220 | 178.143 | 238,010 | 16,666 | 4.049 | 16,280 | 2,846,340 | 200,000 | 149,000 | 149,744 | 38,262,875 | 29,066,423 | 192,572 | 50,841,622 | 1 |
| 3 | Atacama | 44.823 | 26.933 | 19 | 25.478 | 56.225 | 1 | 3.932 | 1 | 9 | 18.479 | 3 | 32.437 | - | - | 184.01 | 35 | 1 | - | 0.39 | 0.197 | 66.841 | 0 | 0.488 | 17,068,077 | 0.533 | 21,705 | 3,125 | 965 | 14 | 504 | 6.4 | 10.2 | 15.727 | 3 | 29.3 | 17,860 | 145.477 | 53,046 | 3,456 | 3.932 | 11,614 | 556,668 | 94,100 | 187,035 | - | 35,948,639 | 16,986,593 | 233,225 | 30,224,699 | 2 |
| 4 | Coquimbo | 37.632 | 25.165 | 9 | 17.805 | 47.745 | 3 | 3.316 | 3 | 13 | 6.963 | 3 | 14.091 | - | 1 | 150.06 | 72 | 3 | - | 0.288 | 0.218 | 155.833 | 0 | 0.396 | 20,225,710 | 0.449 | 69,974 | 4,203 | 1,831 | 100 | 1,082 | 6 | 12.3 | 11.605 | 3 | 29 | 1,431 | 112.73 | 275,447 | 2,229 | 1.658 | 11,056 | 5,364,222 | 189,900 | 72,917 | - | 64,917,630 | 33,222,779 | 673,800 | 44,790,979 | 3 |
| 5 | Valparaíso | 52.408 | 47.05 | 16 | 27.756 | 55.85 | 9 | 1.948 | 12 | 44 | 12.209 | 3 | 18.71 | 2 | 8 | 230.65 | 179 | 3 | 5 | 0.311 | 0.291 | 87.671 | 1.948 | 0.432 | 24,137,492 | 0.445 | 236,177 | 19,176 | 6,765 | 166 | 2,761 | 7.5 | 11.6 | 50.654 | 10 | 10.7 | 16,641 | 143.52 | 1,557,887 | 4,287 | 1.299 | 5,649 | 3,058,423 | 129,481 | 63,996 | 1,079,295 | 101,802,434 | 24,239,428 | 811,772 | 51,647,041 | 1 |
| 6 | Metropolitana | 9.223 | 16.914 | 47 | 25.508 | 65.499 | 0 | 0 | 21 | 42 | 4.834 | 4 | 14.717 | 5 | 33 | 331.61 | 507 | 39 | 1 | 0.372 | 0.295 | 65.334 | 0.66 | 0.47 | 60,400,444 | 0.424 | 1,146,510 | 53,517 | 23,242 | 7,605 | 9,385 | 7.7 | 8.8 | 39.596 | 102 | 12.3 | 147,198 | 1295.113 | 7,307,884 | 6,196 | 0.33 | 19,352 | 13,709,951 | 56,982 | - | - | 230,562,951 | 43,552,270 | 62,052 | 90,875,903 | 1 |
| 7 | O'Higgins | 18.959 | 49.422 | 6 | 19.946 | 43.427 | 0 | 2.562 | 3 | 39 | 9.992 | 2 | 14.95 | 1 | 2 | 117.3 | 114 | 3 | 7 | 0.203 | 0.22 | 51.241 | 0 | 0.396 | 18,182,163 | 0.477 | 111,020 | 4,894 | 2,451 | 762 | 1,136 | 6 | 9.9 | 11.529 | 7 | 10.2 | 26,831 | 95.805 | 265,601 | 3,056 | 1.281 | 4,700 | 754,000 | 179,000 | 6,000 | 345,000 | 50,200,910 | 17,850,727 | 444,491 | 43,045,390 | 1 |
| 8 | Maule | 15.637 | 19.943 | 10 | 23.852 | 35.899 | 9 | 1.101 | 1 | 24 | 8.7 | 5 | 25.383 | - | 3 | 109.73 | 130 | 0 | 2 | 0.245 | 0.227 | 27.53 | 1.101 | 0.427 | 25,936,948 | 0.53 | 124,820 | 6,018 | 4,771 | 194 | 1,875 | 6.4 | 15.8 | 11.012 | 5 | 10.7 | 1,371 | 48.346 | 165,417 | 1,311 | 1.101 | 7,183 | 647,510 | 168,850 | 8,000 | 218,000 | 97,730,159 | 19,292,650 | 486,500 | 48,826,193 | 2 |
| 9 | Biobío | 14.128 | 14.004 | 16 | 27.16 | 38.194 | 2 | 1.612 | 6 | 26 | 1.612 | 3 | 20.531 | 1 | 3 | 162.84 | 211 | 1 | - | 0.36 | 0.31 | 63.925 | 0 | 0.42 | 41,208,340 | 0.41 | 292,281 | 19,222 | 5,713 | 37 | 3,193 | 7.9 | 15.8 | 12.892 | 12 | 11.8 | 15,518 | 188.607 | 305,920 | 4,475 | 1.074 | 7,362 | 3,548,528 | 317,000 | 44,000 | 319,660 | 137,998,425 | 28,228,357 | 1,646,127 | 88,172,439 | 3 |
| 10 | Araucanía | 15.986 | 15.687 | 25 | 25.887 | 40.826 | 23 | 2.3 | 2 | 16 | 9.085 | 13 | 22.736 | 4 | 1 | 135.88 | 118 | 5 | - | 0.381 | 0.288 | 78.203 | 1.15 | 0.369 | 29,000,528 | 0.469 | 140,063 | 8,685 | 3,161 | 323 | 1,550 | 8 | 18.1 | 20.701 | 4 | 30.3 | 5,856 | 232.887 | 395,583 | 372 | 1.15 | 9,488 | 1,842,375 | 108,799 | 27,507 | - | 67,212,297 | 31,050,589 | - | 54,529,735 | 2 |
| 11 | Los Ríos | 34.512 | 26.207 | 9 | 30.472 | 42.649 | 2 | 2.806 | 1 | 40 | 8.698 | 12 | 0 | 1 | - | 191.32 | 44 | 1 | - | 0.274 | 0.27 | 101.011 | 0 | 0.298 | 15,834,434 | 0.44 | 22,076 | 3,235 | 818 | 29 | 773 | 6.4 | 14.3 | 11.223 | 4 | 11.3 | 6,171 | 51.623 | 71,200 | 71 | 2.806 | 7,109 | 1,476,220 | 253,292 | 35,000 | 13,000 | 49,376,288 | 22,065,484 | 486,875 | 32,409,855 | 1 |
| 12 | Los Lagos | 36.415 | 27.36 | 24 | 22.644 | 47.298 | 2 | 4.186 | 2 | 27 | 20.928 | 8 | 25.728 | 2 | 2 | 162.58 | 86 | 6 | - | 0.323 | 0.201 | 104.641 | 0 | 0.374 | 19,901,362 | 0.426 | 85,949 | 8,729 | 2,023 | 192 | 1,409 | 3.5 | 11.8 | 23.719 | 2 | 8 | 2,160 | 994.622 | 122,157 | 2,251 | 4.186 | 4,708 | 7,108,761 | 264,511 | 96,915 | 63,500 | 101,170,739 | 40,816,121 | 737,875 | 62,581,190 | 2 |
| 13 | Aysén | 76.509 | 40.987 | 15 | 27.543 | 43.72 | 0 | 10.93 | 0 | 57 | 67.765 | 4 | 24.264 | 1 | - | 170.81 | 20 | 0 | - | 0.351 | 0.256 | 174.879 | 10.93 | - | 15,972,532 | 0.445 | 14,119 | 3,349 | 422 | - | 469 | 4.4 | 5.2 | 32.79 | 1 | 7 | 1,953 | 106.083 | 13,667 | 379 | 10.93 | 5,660 | 200,654 | 65,000 | 454,500 | 536,488 | 32,558,406 | 13,692,259 | 794,614 | 29,462,726 | 2 |
| 14 | Magallanes y Antártica | 59.008 | 59.34 | 12 | 34.676 | 71.606 | 0 | 13.26 | 1 | 15 | 137.244 | 0 | 18.366 | 1 | 2 | 297.25 | 21 | 3 | - | 0.283 | 0.18 | 53.041 | 6.63 | 0.367 | 20,075,842 | 0.414 | 8,659 | 2,527 | 660 | - | 216 | 5.4 | 6.1 | 86.192 | 1 | 22 | 616 | 5.115 | 322,800 | 397 | 13.26 | 4,217 | 558,678 | 266,000 | 525,000 | 32,100 | 30,955,753 | 18,036,736 | 357,752 | 30,288,105 | 1 |
In [59]:
# Remove $ symbol
chile_data_2 = chile_data_2.replace(r'[<$]', '', regex = True)
# Remove commas from numbers
chile_data_2 = chile_data_2.replace(',','', regex = True)
# Remove `-` character
chile_data_2 = chile_data_2.replace('-','', regex = True)
# Replace empty values with NaNs
chile_data_2 = chile_data_2.replace(r'^\s*$', np.nan, regex = True)
# Check NaNs in the dataset again
chile_data_2
Out[59]:
| Region | Density of restaurants and other food services per 100,000 inhabitants | Density of People employed in restaurants and the like per 10,000 inhabitants | Car rental companies | Densidad de camas en hospitales por cada 10.000 habitantes | Density of beds in hospitals per 10,000 inhabitants | Number of spas | Density of gambling casinos per million inhabitants | Number of golf courses | Number of craft centers | Density of tour guides per 100,000 inhabitants | Number of thermal centers | Density of Sports Facilities and Venues per 10,000 inhabitants | Number of Winter Centers | Number of shopping centers | Penetration of telephone lines in service per 100 inhabitants | Density of service stations | Number of tour-operator companies certified with the tourism quality seal | Number of Vineyards and Wine Routes | Perception of exposure to crime (%) | Percentage of victimized households with at least one victim | Density of homicides per million inhabitants | Density of crimes against public health per million inhabitants | Black figure index | Budget for public safety (Thousands of $) | Percentage of households that reported at least one crime | Number of declared crimes | Number of crimes investigated | Number of accidents (roads, air and waterways) | Illegal commerce | Number of Carabineros | Unemployment rate | Poverty rate | Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants | Number of strikes carried out | Average (days) duration of a strike | Person-day cost of a strike | Density of Bank Branches per million inhabitants | Floating population | Volume of exports | Density of Tourist Information Offices per million inhabitants | Number of visits to Tourist Information Offices | Average monthly global searches by tourist attraction on the internet | National tourism promotion budget (Thousands of USD) | International tourism promotion budget (Thousands of USD) | FNDR resources allocated to promotion (Thousands of USD) | Investments in public infrastructure made by the Ministry of Public Works | Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) | Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population | Funds obtained from FNRD (Thousands of pesos) | Number of regional strategic development plans | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Arica y Parinacota | 35.857 | 19.563 | 12 | 21.619 | 43.766 | 0 | 5.273 | 1 | 20 | 20.038 | 5 | 0 | NaN | NaN | 240.55 | 17 | 6 | NaN | 0.318 | 0.26 | 42.184 | 15.819 | 0.513 | 11884613 | 0.426 | 26467 | 2387 | 981 | 319 | 265 | 7.3 | 8.2 | 84.369 | 1 | 11 | 253 | 94.915 | 45560 | 137 | 5.273 | 6517 | 1060490 | 193364 | 118413 | 92641 | 36370835 | 11757543 | 446922 | 24729573 | 1 |
| 1 | Tarapacá | 25.947 | 26.616 | 2 | 18.958 | 60.264 | 0 | 4.185 | 3 | 9 | 22.18 | 5 | 40.134 | NaN | 2 | 213.07 | 28 | 0 | NaN | 0.421 | 0.468 | 87.884 | 0 | 0.516 | 14740693 | 0.398 | 76896 | 1862 | 1105 | 593 | 592 | 4.8 | 13.4 | 485.457 | 6 | 14.9 | 59745 | 138.104 | 83265 | 3236 | 4.185 | 7600 | 1750481 | 74404 | 13600 | NaN | 42364100 | 12796789 | 355208 | 29714382 | 2 |
| 2 | Antofagasta | 37.248 | 35.446 | 22 | 29.96 | 69.233 | 3 | 4.049 | 3 | 36 | 20.446 | 2 | 14.292 | NaN | 3 | 259.23 | 39 | 2 | NaN | 0.546 | 0.314 | 109.315 | 0 | 0.381 | 22383612 | 0.495 | 123017 | 4228 | 2324 | 7 | 765 | 6.2 | 7.3 | 12.146 | 13 | 9.6 | 30220 | 178.143 | 238010 | 16666 | 4.049 | 16280 | 2846340 | 200000 | 149000 | 149744 | 38262875 | 29066423 | 192572 | 50841622 | 1 |
| 3 | Atacama | 44.823 | 26.933 | 19 | 25.478 | 56.225 | 1 | 3.932 | 1 | 9 | 18.479 | 3 | 32.437 | NaN | NaN | 184.01 | 35 | 1 | NaN | 0.39 | 0.197 | 66.841 | 0 | 0.488 | 17068077 | 0.533 | 21705 | 3125 | 965 | 14 | 504 | 6.4 | 10.2 | 15.727 | 3 | 29.3 | 17860 | 145.477 | 53046 | 3456 | 3.932 | 11614 | 556668 | 94100 | 187035 | NaN | 35948639 | 16986593 | 233225 | 30224699 | 2 |
| 4 | Coquimbo | 37.632 | 25.165 | 9 | 17.805 | 47.745 | 3 | 3.316 | 3 | 13 | 6.963 | 3 | 14.091 | NaN | 1 | 150.06 | 72 | 3 | NaN | 0.288 | 0.218 | 155.833 | 0 | 0.396 | 20225710 | 0.449 | 69974 | 4203 | 1831 | 100 | 1082 | 6 | 12.3 | 11.605 | 3 | 29 | 1431 | 112.73 | 275447 | 2229 | 1.658 | 11056 | 5364222 | 189900 | 72917 | NaN | 64917630 | 33222779 | 673800 | 44790979 | 3 |
| 5 | Valparaíso | 52.408 | 47.05 | 16 | 27.756 | 55.85 | 9 | 1.948 | 12 | 44 | 12.209 | 3 | 18.71 | 2 | 8 | 230.65 | 179 | 3 | 5 | 0.311 | 0.291 | 87.671 | 1.948 | 0.432 | 24137492 | 0.445 | 236177 | 19176 | 6765 | 166 | 2761 | 7.5 | 11.6 | 50.654 | 10 | 10.7 | 16641 | 143.52 | 1557887 | 4287 | 1.299 | 5649 | 3058423 | 129481 | 63996 | 1079295 | 101802434 | 24239428 | 811772 | 51647041 | 1 |
| 6 | Metropolitana | 9.223 | 16.914 | 47 | 25.508 | 65.499 | 0 | 0 | 21 | 42 | 4.834 | 4 | 14.717 | 5 | 33 | 331.61 | 507 | 39 | 1 | 0.372 | 0.295 | 65.334 | 0.66 | 0.47 | 60400444 | 0.424 | 1146510 | 53517 | 23242 | 7605 | 9385 | 7.7 | 8.8 | 39.596 | 102 | 12.3 | 147198 | 1295.113 | 7307884 | 6196 | 0.33 | 19352 | 13709951 | 56982 | NaN | NaN | 230562951 | 43552270 | 62052 | 90875903 | 1 |
| 7 | O'Higgins | 18.959 | 49.422 | 6 | 19.946 | 43.427 | 0 | 2.562 | 3 | 39 | 9.992 | 2 | 14.95 | 1 | 2 | 117.3 | 114 | 3 | 7 | 0.203 | 0.22 | 51.241 | 0 | 0.396 | 18182163 | 0.477 | 111020 | 4894 | 2451 | 762 | 1136 | 6 | 9.9 | 11.529 | 7 | 10.2 | 26831 | 95.805 | 265601 | 3056 | 1.281 | 4700 | 754000 | 179000 | 6000 | 345000 | 50200910 | 17850727 | 444491 | 43045390 | 1 |
| 8 | Maule | 15.637 | 19.943 | 10 | 23.852 | 35.899 | 9 | 1.101 | 1 | 24 | 8.7 | 5 | 25.383 | NaN | 3 | 109.73 | 130 | 0 | 2 | 0.245 | 0.227 | 27.53 | 1.101 | 0.427 | 25936948 | 0.53 | 124820 | 6018 | 4771 | 194 | 1875 | 6.4 | 15.8 | 11.012 | 5 | 10.7 | 1371 | 48.346 | 165417 | 1311 | 1.101 | 7183 | 647510 | 168850 | 8000 | 218000 | 97730159 | 19292650 | 486500 | 48826193 | 2 |
| 9 | Biobío | 14.128 | 14.004 | 16 | 27.16 | 38.194 | 2 | 1.612 | 6 | 26 | 1.612 | 3 | 20.531 | 1 | 3 | 162.84 | 211 | 1 | NaN | 0.36 | 0.31 | 63.925 | 0 | 0.42 | 41208340 | 0.41 | 292281 | 19222 | 5713 | 37 | 3193 | 7.9 | 15.8 | 12.892 | 12 | 11.8 | 15518 | 188.607 | 305920 | 4475 | 1.074 | 7362 | 3548528 | 317000 | 44000 | 319660 | 137998425 | 28228357 | 1646127 | 88172439 | 3 |
| 10 | Araucanía | 15.986 | 15.687 | 25 | 25.887 | 40.826 | 23 | 2.3 | 2 | 16 | 9.085 | 13 | 22.736 | 4 | 1 | 135.88 | 118 | 5 | NaN | 0.381 | 0.288 | 78.203 | 1.15 | 0.369 | 29000528 | 0.469 | 140063 | 8685 | 3161 | 323 | 1550 | 8 | 18.1 | 20.701 | 4 | 30.3 | 5856 | 232.887 | 395583 | 372 | 1.15 | 9488 | 1842375 | 108799 | 27507 | NaN | 67212297 | 31050589 | NaN | 54529735 | 2 |
| 11 | Los Ríos | 34.512 | 26.207 | 9 | 30.472 | 42.649 | 2 | 2.806 | 1 | 40 | 8.698 | 12 | 0 | 1 | NaN | 191.32 | 44 | 1 | NaN | 0.274 | 0.27 | 101.011 | 0 | 0.298 | 15834434 | 0.44 | 22076 | 3235 | 818 | 29 | 773 | 6.4 | 14.3 | 11.223 | 4 | 11.3 | 6171 | 51.623 | 71200 | 71 | 2.806 | 7109 | 1476220 | 253292 | 35000 | 13000 | 49376288 | 22065484 | 486875 | 32409855 | 1 |
| 12 | Los Lagos | 36.415 | 27.36 | 24 | 22.644 | 47.298 | 2 | 4.186 | 2 | 27 | 20.928 | 8 | 25.728 | 2 | 2 | 162.58 | 86 | 6 | NaN | 0.323 | 0.201 | 104.641 | 0 | 0.374 | 19901362 | 0.426 | 85949 | 8729 | 2023 | 192 | 1409 | 3.5 | 11.8 | 23.719 | 2 | 8 | 2160 | 994.622 | 122157 | 2251 | 4.186 | 4708 | 7108761 | 264511 | 96915 | 63500 | 101170739 | 40816121 | 737875 | 62581190 | 2 |
| 13 | Aysén | 76.509 | 40.987 | 15 | 27.543 | 43.72 | 0 | 10.93 | 0 | 57 | 67.765 | 4 | 24.264 | 1 | NaN | 170.81 | 20 | 0 | NaN | 0.351 | 0.256 | 174.879 | 10.93 | NaN | 15972532 | 0.445 | 14119 | 3349 | 422 | NaN | 469 | 4.4 | 5.2 | 32.79 | 1 | 7 | 1953 | 106.083 | 13667 | 379 | 10.93 | 5660 | 200654 | 65000 | 454500 | 536488 | 32558406 | 13692259 | 794614 | 29462726 | 2 |
| 14 | Magallanes y Antártica | 59.008 | 59.34 | 12 | 34.676 | 71.606 | 0 | 13.26 | 1 | 15 | 137.244 | 0 | 18.366 | 1 | 2 | 297.25 | 21 | 3 | NaN | 0.283 | 0.18 | 53.041 | 6.63 | 0.367 | 20075842 | 0.414 | 8659 | 2527 | 660 | NaN | 216 | 5.4 | 6.1 | 86.192 | 1 | 22 | 616 | 5.115 | 322800 | 397 | 13.26 | 4217 | 558678 | 266000 | 525000 | 32100 | 30955753 | 18036736 | 357752 | 30288105 | 1 |
In [60]:
chile_data_2.isnull().sum(axis = 0)
#checking for NaN/ Null
Out[60]:
Region 0 Density of restaurants and other food services per 100,000 inhabitants 0 Density of People employed in restaurants and the like per 10,000 inhabitants 0 Car rental companies 0 Densidad de camas en hospitales por cada 10.000 habitantes 0 Density of beds in hospitals per 10,000 inhabitants 0 Number of spas 0 Density of gambling casinos per million inhabitants 0 Number of golf courses 0 Number of craft centers 0 Density of tour guides per 100,000 inhabitants 0 Number of thermal centers 0 Density of Sports Facilities and Venues per 10,000 inhabitants 0 Number of Winter Centers 6 Number of shopping centers 4 Penetration of telephone lines in service per 100 inhabitants 0 Density of service stations 0 Number of tour-operator companies certified with the tourism quality seal 0 Number of Vineyards and Wine Routes 11 Perception of exposure to crime (%) 0 Percentage of victimized households with at least one victim 0 Density of homicides per million inhabitants 0 Density of crimes against public health per million inhabitants 0 Black figure index 1 Budget for public safety (Thousands of $) 0 Percentage of households that reported at least one crime 0 Number of declared crimes 0 Number of crimes investigated 0 Number of accidents (roads, air and waterways) 0 Illegal commerce 2 Number of Carabineros 0 Unemployment rate 0 Poverty rate 0 Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants 0 Number of strikes carried out 0 Average (days) duration of a strike 0 Person-day cost of a strike 0 Density of Bank Branches per million inhabitants 0 Floating population 0 Volume of exports 0 Density of Tourist Information Offices per million inhabitants 0 Number of visits to Tourist Information Offices 0 Average monthly global searches by tourist attraction on the internet 0 National tourism promotion budget (Thousands of USD) 0 International tourism promotion budget (Thousands of USD) 1 FNDR resources allocated to promotion (Thousands of USD) 5 Investments in public infrastructure made by the Ministry of Public Works 0 Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) 0 Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population 1 Funds obtained from FNRD (Thousands of pesos) 0 Number of regional strategic development plans 0 dtype: int64
we have NaNs in 8 columns:
- Number of Winter Centers (6 NaNs)
- Number of shopping centers (4 NaNs)
- Number of Vineyards and Wine Routes (11 NaNs)
- Black figure index (1 NaN)
- Illegal commerce (2 NaNs)
- International tourism promotion budget (Thousands of USD) (1 NaN)
- FNDR resources allocated to promotion (Thousands of USD) (5 NaNs)
- Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population (1 NaN)
In our case we must drop some variables:
- Number of Winter Centers (6 NaNs)
- Number of shopping centers (4 NaNs)
- Number of Vineyards and Wine Routes (11 NaNs)
- FNDR resources allocated to promotion (Thousands of USD) (5 NaNs)
These are variables where 30-70 percent of data is missing. It does not makes sense to impute empty data here plus in general the imputation is a bad idea here, we have only 15 rows here and each missing data point in a row can skew our results.
In [61]:
# Drop 4 columns with many NaNs
chile_data_2 = chile_data_2 = chile_data_2.drop(['Number of Winter Centers',
'Number of shopping centers',
'Number of Vineyards and Wine Routes',
'FNDR resources allocated to promotion (Thousands of USD)'], axis = 1)
In [62]:
chile_data_2.shape
Out[62]:
(15, 47)
In [63]:
# Impute data in four columns
imputer = SimpleImputer(missing_values = np.nan, strategy = 'mean')
chile_data_2[['Illegal commerce',
'Black figure index',
'International tourism promotion budget (Thousands of USD)',
'Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population']] = imputer.fit_transform(chile_data_2[['Illegal commerce',
'Black figure index',
'International tourism promotion budget (Thousands of USD)',
'Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population']])
In [64]:
# Check NaNs in the dataset
chile_data_2.isnull().sum(axis = 0)
Out[64]:
Region 0 Density of restaurants and other food services per 100,000 inhabitants 0 Density of People employed in restaurants and the like per 10,000 inhabitants 0 Car rental companies 0 Densidad de camas en hospitales por cada 10.000 habitantes 0 Density of beds in hospitals per 10,000 inhabitants 0 Number of spas 0 Density of gambling casinos per million inhabitants 0 Number of golf courses 0 Number of craft centers 0 Density of tour guides per 100,000 inhabitants 0 Number of thermal centers 0 Density of Sports Facilities and Venues per 10,000 inhabitants 0 Penetration of telephone lines in service per 100 inhabitants 0 Density of service stations 0 Number of tour-operator companies certified with the tourism quality seal 0 Perception of exposure to crime (%) 0 Percentage of victimized households with at least one victim 0 Density of homicides per million inhabitants 0 Density of crimes against public health per million inhabitants 0 Black figure index 0 Budget for public safety (Thousands of $) 0 Percentage of households that reported at least one crime 0 Number of declared crimes 0 Number of crimes investigated 0 Number of accidents (roads, air and waterways) 0 Illegal commerce 0 Number of Carabineros 0 Unemployment rate 0 Poverty rate 0 Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants 0 Number of strikes carried out 0 Average (days) duration of a strike 0 Person-day cost of a strike 0 Density of Bank Branches per million inhabitants 0 Floating population 0 Volume of exports 0 Density of Tourist Information Offices per million inhabitants 0 Number of visits to Tourist Information Offices 0 Average monthly global searches by tourist attraction on the internet 0 National tourism promotion budget (Thousands of USD) 0 International tourism promotion budget (Thousands of USD) 0 Investments in public infrastructure made by the Ministry of Public Works 0 Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) 0 Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population 0 Funds obtained from FNRD (Thousands of pesos) 0 Number of regional strategic development plans 0 dtype: int64
In [65]:
# Converting into numeric
chile_data_2 = chile_data_2.set_index('Region')
# Select columns
cols = chile_data_2.loc[:, chile_data_2.dtypes == np.object].columns
# Convert to numeric
chile_data_2[cols] = chile_data_2[cols].apply(pd.to_numeric, errors = 'coerce', axis = 1)
# Now all our columns are integers or floats
chile_data_2 = chile_data_2.reset_index()
In [66]:
chile_data_2.dtypes
Out[66]:
Region object Density of restaurants and other food services per 100,000 inhabitants float64 Density of People employed in restaurants and the like per 10,000 inhabitants float64 Car rental companies float64 Densidad de camas en hospitales por cada 10.000 habitantes float64 Density of beds in hospitals per 10,000 inhabitants float64 Number of spas float64 Density of gambling casinos per million inhabitants float64 Number of golf courses float64 Number of craft centers float64 Density of tour guides per 100,000 inhabitants float64 Number of thermal centers float64 Density of Sports Facilities and Venues per 10,000 inhabitants float64 Penetration of telephone lines in service per 100 inhabitants float64 Density of service stations float64 Number of tour-operator companies certified with the tourism quality seal float64 Perception of exposure to crime (%) float64 Percentage of victimized households with at least one victim float64 Density of homicides per million inhabitants float64 Density of crimes against public health per million inhabitants float64 Black figure index float64 Budget for public safety (Thousands of $) float64 Percentage of households that reported at least one crime float64 Number of declared crimes float64 Number of crimes investigated float64 Number of accidents (roads, air and waterways) float64 Illegal commerce float64 Number of Carabineros float64 Unemployment rate float64 Poverty rate float64 Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants float64 Number of strikes carried out float64 Average (days) duration of a strike float64 Person-day cost of a strike float64 Density of Bank Branches per million inhabitants float64 Floating population float64 Volume of exports float64 Density of Tourist Information Offices per million inhabitants float64 Number of visits to Tourist Information Offices float64 Average monthly global searches by tourist attraction on the internet float64 National tourism promotion budget (Thousands of USD) float64 International tourism promotion budget (Thousands of USD) float64 Investments in public infrastructure made by the Ministry of Public Works float64 Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) float64 Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population float64 Funds obtained from FNRD (Thousands of pesos) float64 Number of regional strategic development plans float64 dtype: object
2. Principal Component Analysis
In [67]:
# Finally, we need to standardize data for applying PCA
# Create a copy
chile_data_s_2 = chile_data_2.copy()
# Standardize
scaler = StandardScaler()
chile_data_s_2.loc[:, chile_data_s_2.columns != 'Region'] = scaler.fit_transform(chile_data_s_2.loc[:, chile_data_s_2.columns != 'Region'])
# Set region as an index column
chile_data_s_2 = chile_data_s_2.set_index('Region')
pass
In [68]:
chile_data_s_2
Out[68]:
| Density of restaurants and other food services per 100,000 inhabitants | Density of People employed in restaurants and the like per 10,000 inhabitants | Car rental companies | Densidad de camas en hospitales por cada 10.000 habitantes | Density of beds in hospitals per 10,000 inhabitants | Number of spas | Density of gambling casinos per million inhabitants | Number of golf courses | Number of craft centers | Density of tour guides per 100,000 inhabitants | Number of thermal centers | Density of Sports Facilities and Venues per 10,000 inhabitants | Penetration of telephone lines in service per 100 inhabitants | Density of service stations | Number of tour-operator companies certified with the tourism quality seal | Perception of exposure to crime (%) | Percentage of victimized households with at least one victim | Density of homicides per million inhabitants | Density of crimes against public health per million inhabitants | Black figure index | Budget for public safety (Thousands of $) | Percentage of households that reported at least one crime | Number of declared crimes | Number of crimes investigated | Number of accidents (roads, air and waterways) | Illegal commerce | Number of Carabineros | Unemployment rate | Poverty rate | Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants | Number of strikes carried out | Average (days) duration of a strike | Person-day cost of a strike | Density of Bank Branches per million inhabitants | Floating population | Volume of exports | Density of Tourist Information Offices per million inhabitants | Number of visits to Tourist Information Offices | Average monthly global searches by tourist attraction on the internet | National tourism promotion budget (Thousands of USD) | International tourism promotion budget (Thousands of USD) | Investments in public infrastructure made by the Ministry of Public Works | Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) | Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population | Funds obtained from FNRD (Thousands of pesos) | Number of regional strategic development plans | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | ||||||||||||||||||||||||||||||||||||||||||||||
| Arica y Parinacota | 0.087073 | -0.798010 | -0.411459 | -0.820845 | -0.633616 | -0.607010 | 0.342739 | -0.560316 | -0.554658 | -0.135893 | 0.057354 | -1.871499 | 0.695488 | -0.749525 | 0.121472 | -0.248797 | -0.093065 | -1.087756 | 2.859839 | 1.648551 | -0.996415 | -0.652770 | -0.512578 | -0.565722 | -0.513953 | -2.587452e-01 | -0.663000 | 0.812831 | -0.834653 | 0.204305 | -0.433573 | -0.533574 | -0.597159 | -0.448859 | -0.392595 | -0.771859 | 0.417721 | -0.484381 | -0.554876 | 0.288149 | -6.791934e-02 | -0.731979 | -1.298074 | -0.291691 | -1.151570 | -0.953463 |
| Tarapacá | -0.462238 | -0.260925 | -1.375815 | -1.416782 | 0.849682 | -0.607010 | 0.025557 | -0.186772 | -1.336868 | -0.072248 | 0.057354 | 2.063212 | 0.255363 | -0.658989 | -0.521615 | 1.049823 | 2.963394 | 0.083237 | -0.549390 | 1.700415 | -0.757515 | -1.353955 | -0.328183 | -0.606462 | -0.491469 | -1.099479e-01 | -0.515181 | -1.141089 | 0.586808 | 3.657754 | -0.229057 | -0.038898 | 1.017526 | -0.328069 | -0.371529 | 0.000349 | 0.115821 | -0.228403 | -0.353907 | -1.225117 | -7.595427e-01 | -0.616910 | -1.189453 | -0.545976 | -0.899207 | 0.476731 |
| Antofagasta | 0.164176 | 0.411478 | 0.552897 | 1.047141 | 1.656064 | -0.101168 | -0.014090 | -0.186772 | 0.583102 | -0.123770 | -0.802955 | -0.470321 | 0.994671 | -0.568454 | -0.307253 | 2.625818 | 0.700439 | 0.632374 | -0.549390 | -0.633488 | -0.118212 | 1.075151 | -0.159541 | -0.422858 | -0.270437 | -4.281786e-01 | -0.436977 | -0.046894 | -1.080675 | -0.417550 | 0.057264 | -0.711150 | 0.216182 | -0.216088 | -0.285070 | 3.346832 | 0.078083 | 1.823205 | -0.034723 | 0.372564 | 1.339133e-01 | -0.695652 | 0.511042 | -0.996899 | 0.170390 | -0.953463 |
| Atacama | 0.584058 | -0.236785 | 0.263591 | 0.043387 | 0.486545 | -0.438396 | -0.048199 | -0.560316 | -1.336868 | -0.182215 | -0.516185 | 1.308603 | -0.210069 | -0.601376 | -0.414434 | 0.658976 | -1.018820 | -0.455958 | -0.549390 | 1.216346 | -0.562837 | 2.026759 | -0.529991 | -0.508452 | -0.516855 | -4.243772e-01 | -0.554961 | 0.109420 | -0.287937 | -0.386717 | -0.351766 | 1.787599 | -0.119284 | -0.307448 | -0.388412 | 0.055168 | 0.045618 | 0.720348 | -0.701621 | -0.974568 | 3.848926e-01 | -0.740085 | -0.751536 | -0.884185 | -0.873371 | 0.476731 |
| Coquimbo | 0.185461 | -0.371418 | -0.700765 | -1.674999 | -0.275873 | -0.101168 | -0.227780 | -0.186772 | -1.052428 | -0.524389 | -0.516185 | -0.490027 | -0.753819 | -0.296847 | -0.200071 | -0.627036 | -0.710235 | 1.824327 | -0.549390 | -0.374165 | -0.298713 | -0.076796 | -0.353494 | -0.424798 | -0.359829 | -3.776744e-01 | -0.293679 | -0.203208 | 0.286115 | -0.422208 | -0.351766 | 1.749547 | -0.565187 | -0.399034 | -0.264153 | -0.250575 | -0.585376 | 0.588459 | 0.698645 | 0.244084 | -3.681311e-01 | -0.183891 | 0.945462 | 0.337347 | -0.135932 | 1.906925 |
| Valparaíso | 1.004495 | 1.295121 | -0.025716 | 0.553550 | 0.452829 | 0.910515 | -0.626589 | 1.494175 | 1.151982 | -0.368515 | -0.516185 | -0.037183 | 0.536928 | 0.583817 | -0.200071 | -0.337053 | 0.362465 | 0.077779 | -0.129568 | 0.248209 | 0.028493 | -0.176966 | 0.254231 | 0.737122 | 0.534818 | -3.418327e-01 | 0.465305 | 0.969144 | 0.094764 | -0.085988 | -0.065445 | -0.571626 | -0.152369 | -0.312921 | 0.452371 | 0.262237 | -0.684992 | -0.689542 | 0.027050 | -0.524494 | -4.269976e-01 | 0.524283 | 0.006527 | 0.719887 | 0.211166 | -0.953463 |
| Metropolitana | -1.389249 | -0.999731 | 2.963788 | 0.050106 | 1.320349 | -0.607010 | -1.194483 | 3.175121 | 1.009762 | -0.587648 | -0.229416 | -0.428654 | 2.153925 | 3.283423 | 3.658450 | 0.432033 | 0.421243 | -0.494572 | -0.407151 | 0.905159 | 3.061756 | -0.702855 | 3.582885 | 3.402018 | 3.522474 | 3.697959e+00 | 3.459654 | 1.125458 | -0.670638 | -0.181200 | 3.697638 | -0.368682 | 3.391107 | 2.907840 | 3.665005 | 0.737921 | -0.953872 | 2.549305 | 3.129454 | -1.446739 | -9.602287e-17 | 2.996435 | 2.025096 | -1.358777 | 2.197183 | -0.953463 |
| O'Higgins | -0.849583 | 1.475749 | -0.990072 | -1.195517 | -0.664094 | -0.607010 | -0.447591 | -0.186772 | 0.796432 | -0.434389 | -0.802955 | -0.405811 | -1.278511 | 0.048834 | -0.200071 | -1.698713 | -0.680846 | -0.855684 | -0.549390 | -0.374165 | -0.469648 | 0.624389 | -0.203408 | -0.371176 | -0.247409 | -1.817148e-02 | -0.269268 | -0.203208 | -0.369944 | -0.422863 | -0.188154 | -0.635046 | 0.124200 | -0.446370 | -0.269654 | -0.044503 | -0.689987 | -0.913848 | -0.644146 | 0.105427 | -8.096924e-01 | -0.466447 | -0.661217 | -0.298431 | -0.224305 | -0.953463 |
| Maule | -1.033721 | -0.769073 | -0.604330 | -0.320759 | -1.340920 | 0.910515 | -0.873512 | -0.560316 | -0.270218 | -0.472778 | 0.057354 | 0.617034 | -1.399753 | 0.180522 | -0.521615 | -1.169178 | -0.577984 | -1.463242 | -0.312109 | 0.161768 | 0.179011 | 1.951632 | -0.152948 | -0.283952 | 0.173260 | -3.266272e-01 | 0.064793 | 0.109420 | 1.242867 | -0.427314 | -0.269960 | -0.571626 | -0.566815 | -0.579103 | -0.325629 | -0.479322 | -0.739933 | -0.326965 | -0.675162 | -0.023689 | -7.964951e-01 | 0.446097 | -0.510508 | -0.181958 | 0.068356 | 0.476731 |
| Biobío | -1.117365 | -1.221327 | -0.025716 | 0.420075 | -1.134581 | -0.269782 | -0.724542 | 0.373544 | -0.127998 | -0.683383 | -0.516185 | 0.141347 | -0.549132 | 0.847193 | -0.414434 | 0.280737 | 0.641661 | -0.530676 | -0.549390 | 0.040751 | 1.456407 | -1.053447 | 0.459377 | 0.740691 | 0.344066 | -4.118869e-01 | 0.660589 | 1.281772 | 1.242867 | -0.411127 | 0.016361 | -0.432102 | -0.182849 | -0.186823 | -0.247127 | 0.309082 | -0.747425 | -0.284657 | 0.169799 | 1.860898 | -5.589440e-01 | 1.219232 | 0.423448 | 3.033208 | 2.060316 | 1.906925 |
| Araucanía | -1.014376 | -1.093167 | 0.842204 | 0.134984 | -0.897944 | 3.271109 | -0.523971 | -0.373544 | -0.839098 | -0.461338 | 2.351511 | 0.357523 | -0.980929 | 0.081756 | 0.014291 | 0.545505 | 0.318381 | -0.164824 | -0.301548 | -0.840946 | 0.435268 | 0.424050 | -0.097212 | -0.076991 | -0.118670 | -2.565730e-01 | -0.082122 | 1.359928 | 1.871590 | -0.343890 | -0.310863 | 1.914439 | -0.445087 | -0.062981 | -0.197030 | -0.713302 | -0.726337 | 0.217846 | -0.327141 | -0.787585 | -6.677754e-01 | -0.139835 | 0.718426 | 0.000000 | 0.357106 | 0.476731 |
| Los Ríos | 0.012520 | -0.292070 | -0.700765 | 1.161805 | -0.734043 | -0.269782 | -0.376458 | -0.560316 | 0.867542 | -0.472837 | 2.064742 | -1.871499 | -0.092990 | -0.527301 | -0.414434 | -0.803547 | 0.053880 | 0.419596 | -0.549390 | -2.068406 | -0.666027 | -0.302177 | -0.528634 | -0.499916 | -0.543509 | -4.162314e-01 | -0.433361 | 0.109420 | 0.832830 | -0.425497 | -0.310863 | -0.495522 | -0.436538 | -0.569938 | -0.378269 | -0.788305 | -0.266827 | -0.344456 | -0.433789 | 1.050481 | -6.183318e-01 | -0.482279 | -0.220693 | -0.180918 | -0.762745 | -0.953463 |
| Los Lagos | 0.118003 | -0.204269 | 0.745769 | -0.591294 | -0.316062 | -0.269782 | 0.025849 | -0.373544 | -0.056888 | -0.109448 | 0.917663 | 0.650857 | -0.553296 | -0.181620 | 0.121472 | -0.185757 | -0.960042 | 0.512610 | -0.549390 | -0.754505 | -0.325844 | -0.652770 | -0.295081 | -0.073576 | -0.325015 | -3.277133e-01 | -0.145860 | -2.157128 | 0.149436 | -0.317904 | -0.392670 | -0.914094 | -0.545401 | 2.067430 | -0.349799 | -0.245093 | 0.116098 | -0.911957 | 1.206767 | 1.193196 | -2.097769e-01 | 0.512154 | 1.739115 | 0.515001 | 0.764723 | 0.476731 |
| Aysén | 2.340413 | 0.833425 | -0.122152 | 0.505849 | -0.637751 | -0.607010 | 1.991907 | -0.747087 | 2.076412 | 1.282215 | -0.229416 | 0.507328 | -0.421483 | -0.724833 | -0.521615 | 0.167266 | -0.151843 | 2.312351 | 1.806187 | 0.000000 | -0.654476 | -0.176966 | -0.557729 | -0.491070 | -0.615313 | -6.173829e-17 | -0.570783 | -1.453717 | -1.654726 | -0.239801 | -0.433573 | -1.040934 | -0.551019 | -0.417625 | -0.410414 | -0.711558 | 1.987437 | -0.686942 | -0.805315 | -1.344744 | 2.149798e+00 | -0.805176 | -1.095859 | 0.672315 | -0.911947 | 0.476731 |
| Magallanes y Antártica | 1.370333 | 2.231003 | -0.411459 | 2.103300 | 1.869415 | -0.607010 | 2.671164 | -0.560316 | -0.910208 | 3.346638 | -1.376494 | -0.070909 | 1.603608 | -0.716603 | -0.200071 | -0.690076 | -1.268627 | -0.809562 | 0.879473 | -0.875522 | -0.311249 | -0.953278 | -0.577694 | -0.554858 | -0.572158 | -6.173829e-17 | -0.685150 | -0.672149 | -1.408704 | 0.220001 | -0.433573 | 0.861667 | -0.587307 | -0.700011 | -0.237696 | -0.707072 | 2.633969 | -1.028010 | -0.701036 | 1.212137 | 2.615002e+00 | -0.835946 | -0.641776 | -0.538923 | -0.870161 | -0.953463 |
Eigenvalues and eigenvectors
In [69]:
# Calculate eigenvalues and vectors
cov_mat = np.cov(chile_data_s_2.T)
eig_val, eig_vec = np.linalg.eig(cov_mat)
# Print
print('Eigenvectors \n%s' %eig_vec)
print('\nEigenvalues \n%s' %eig_val)
Eigenvectors [[ 0.13313201+0.j -0.23035129+0.j 0.0689068 +0.j ... -0.04247971+0.0370457j 0.04872969+0.04915378j 0.04872969-0.04915378j] [ 0.09719407+0.j -0.22771205+0.j 0.08783438+0.j ... 0.02191131-0.02586547j -0.02678085+0.00210744j -0.02678085-0.00210744j] [-0.19581156+0.j -0.06937946+0.j 0.08290993+0.j ... -0.07879693+0.06185894j 0.02710413+0.04667135j 0.02710413-0.04667135j] ... [ 0.03694777+0.j 0.14566667+0.j 0.20091417+0.j ... 0.10080102-0.00846084j -0.02357666-0.00280769j -0.02357666+0.00280769j] [-0.19303555+0.j 0.09406386+0.j 0.13395992+0.j ... -0.02083788+0.03723748j 0.10429246+0.02310865j 0.10429246-0.02310865j] [ 0.02888236+0.j 0.19164569+0.j -0.0470399 +0.j ... 0.00254517-0.02573687j -0.03570821-0.06621232j -0.03570821+0.06621232j]] Eigenvalues [ 1.88351109e+01+0.00000000e+00j 8.20392035e+00+0.00000000e+00j 4.31308598e+00+0.00000000e+00j 3.04535368e+00+0.00000000e+00j 2.83788710e+00+0.00000000e+00j 2.45872307e+00+0.00000000e+00j 2.11826332e+00+0.00000000e+00j 1.95116248e+00+0.00000000e+00j 1.73847859e+00+0.00000000e+00j 1.52051249e+00+0.00000000e+00j 1.03145621e+00+0.00000000e+00j 6.55262136e-01+0.00000000e+00j 3.87450124e-01+0.00000000e+00j 1.89047837e-01+0.00000000e+00j 9.80403397e-16+0.00000000e+00j 7.00757475e-16+1.58504386e-16j 7.00757475e-16-1.58504386e-16j -8.77352586e-16+0.00000000e+00j 5.14456544e-16+2.85945807e-16j 5.14456544e-16-2.85945807e-16j 5.75263855e-16+0.00000000e+00j 4.29763873e-16+0.00000000e+00j 2.95300025e-16+1.15018850e-16j 2.95300025e-16-1.15018850e-16j 2.97378861e-16+5.66592080e-17j 2.97378861e-16-5.66592080e-17j -6.31925937e-16+0.00000000e+00j -4.86908356e-16+1.63779169e-16j -4.86908356e-16-1.63779169e-16j -5.24509357e-16+6.30086846e-17j -5.24509357e-16-6.30086846e-17j -5.07369230e-16+0.00000000e+00j -4.27735497e-16+0.00000000e+00j -3.41983903e-16+0.00000000e+00j -2.68174710e-16+5.21912258e-17j -2.68174710e-16-5.21912258e-17j -2.80060656e-16+0.00000000e+00j 1.22600042e-16+6.64874638e-17j 1.22600042e-16-6.64874638e-17j -1.18016904e-16+4.88284602e-17j -1.18016904e-16-4.88284602e-17j 7.38069176e-17+0.00000000e+00j -9.15956110e-18+5.77505472e-17j -9.15956110e-18-5.77505472e-17j -2.99869362e-17+2.43347586e-17j -2.99869362e-17-2.43347586e-17j]
In [70]:
myPCA = PCA()
x = myPCA.fit(chile_data_s_2)
In [71]:
plt.bar(range(1,len(x.explained_variance_ )+1),x.explained_variance_ratio_)
plt.ylabel('Explained variance')
plt.xlabel('Components')
plt.title('All Principle Components')
Out[71]:
Text(0.5, 1.0, 'All Principle Components')
In [72]:
# Deciding on the number of principal componenets to chose
plt.plot(range(1, len(x.explained_variance_)+1), x.explained_variance_ratio_.cumsum())
plt.ylabel('Explained variance')
plt.xlabel('Components')
pass
In [73]:
# Calculate the numeric values of principal components
x.explained_variance_ratio_.cumsum()
Out[73]:
array([0.38216167, 0.54861803, 0.63612992, 0.6979197 , 0.75550002,
0.80538715, 0.84836641, 0.88795521, 0.92322869, 0.95407967,
0.97500777, 0.98830294, 0.99616425, 1. , 1. ])
will use only first 7 components, which explain 83.6% of variance.
So, instead of 51 columns in our dataset we have 7 uncorrelated components,
Perform PCA with 7 components now and fit the dataset again
</span>
In [74]:
# Initialize PCA with 7 components
myPCA = PCA(n_components = 7)
In [75]:
pca_model = myPCA.fit(chile_data_s_2)
print("The loadings are are \n {}".format(pca_model.components_))
The loadings are are [[-1.33132006e-01 -9.71940725e-02 1.95811562e-01 -1.00633015e-02 5.76552543e-02 6.68292211e-03 -1.39481743e-01 2.19424069e-01 5.81024190e-02 -9.80069137e-02 1.89879021e-03 -1.70435214e-02 1.04726340e-01 2.29038996e-01 2.17724253e-01 4.68836016e-02 5.03568581e-02 -4.16694955e-02 -7.76542633e-02 4.70672529e-02 2.18917075e-01 -3.48170710e-02 2.34362576e-01 2.30352223e-01 2.32391896e-01 2.08386502e-01 2.33117564e-01 1.08427604e-01 1.85849384e-02 -2.62713787e-02 2.27836728e-01 -2.71694372e-02 2.06880041e-01 1.90524998e-01 2.24280863e-01 8.20218888e-02 -1.26775062e-01 1.68544853e-01 2.12374047e-01 -6.44262692e-02 -6.44486770e-02 2.20135638e-01 1.68277229e-01 -3.69477651e-02 1.93035546e-01 -2.88823556e-02] [ 2.30351292e-01 2.27712050e-01 6.93794581e-02 1.71639585e-01 2.63718306e-01 -1.95403178e-01 2.61322689e-01 5.34722980e-02 1.03726706e-01 2.75752726e-01 -1.89730561e-01 -2.92279035e-02 2.71310833e-01 -1.55981324e-02 9.44690831e-02 6.47004869e-02 -4.57325361e-02 5.90127297e-02 1.71328123e-01 3.62242215e-02 -1.54726567e-02 -9.55109858e-02 4.41763502e-02 3.57031601e-02 3.54338216e-02 1.26485791e-01 1.41245990e-02 -1.18670298e-01 -3.29769143e-01 4.00781412e-02 8.45844050e-02 -6.80922600e-02 8.66982272e-02 4.07449335e-02 9.60635484e-02 5.82095023e-02 2.77196673e-01 3.04942383e-02 2.51100716e-02 -5.88335533e-02 3.03379869e-01 -4.85144078e-02 -6.30021675e-02 -1.45666668e-01 -9.40638554e-02 -1.91645687e-01] [-6.89068023e-02 -8.78343802e-02 -8.29099311e-02 -2.21100162e-01 1.75429953e-01 -4.73509563e-02 -5.03528122e-02 -6.02748486e-04 -2.35358257e-01 -7.44154323e-02 -6.48955445e-02 2.77975454e-01 6.80221109e-02 -7.19432839e-02 -1.81128180e-02 2.55891875e-01 3.35197481e-01 -2.02583649e-02 -5.53894206e-02 3.45710584e-01 -8.03622867e-02 1.24990764e-02 -8.39842957e-03 -6.14561946e-02 -2.64249892e-02 1.65549222e-02 -4.84295692e-02 -5.03205763e-02 1.12172842e-02 4.00850615e-01 1.81200330e-02 1.08326713e-01 1.75037033e-01 -3.93184470e-02 -6.03556037e-03 1.19990187e-01 -3.65338808e-02 1.45648520e-01 -3.68609241e-02 -2.82826872e-01 -1.00615717e-01 -9.66729038e-02 -1.52173128e-01 -2.00914174e-01 -1.33959921e-01 4.70399010e-02] [ 2.80784130e-02 2.36007442e-02 1.69679121e-01 2.31695981e-01 2.07537609e-01 2.15617864e-01 -4.08766826e-02 -6.37984227e-02 -6.16156229e-02 -1.85221201e-02 3.17105555e-02 -7.75838797e-03 4.40360519e-02 -7.62541749e-02 -4.72012880e-02 3.08374270e-01 -1.12828098e-01 7.57650951e-02 -2.24282233e-01 -2.21497910e-01 6.08256244e-03 3.61261507e-01 -5.12265667e-02 -7.07351253e-02 -5.07773488e-02 -9.00492965e-02 -7.05745807e-02 9.28359020e-02 -7.85431609e-03 -2.36562438e-01 -2.34242869e-02 2.42081072e-01 -4.86295837e-02 -4.42121739e-02 -4.23739891e-02 3.47946518e-01 -4.50589513e-02 3.09601050e-01 -2.69981782e-02 -5.44818582e-03 3.17126939e-02 -1.12859775e-01 1.63640953e-01 -2.12271012e-01 5.99367880e-03 -6.06961277e-02] [ 1.22165134e-01 -4.58479447e-02 6.17916567e-02 -1.81180662e-02 4.52048821e-02 -6.24925452e-02 1.11906018e-01 -2.41633497e-02 -1.35535320e-02 6.24674818e-02 -7.34807079e-03 2.77051854e-01 -5.78117653e-02 -2.45736508e-02 -6.64597691e-02 2.09205467e-01 9.56615451e-02 3.95013932e-01 -1.74264053e-01 -1.12312703e-01 6.00172642e-02 -2.30829399e-01 -2.21875638e-02 8.05279105e-03 -5.52908133e-02 -6.09412759e-02 -6.10325525e-03 -3.05601935e-01 3.08879941e-02 8.15239030e-02 -4.72735591e-02 2.74397902e-03 -2.96249043e-02 1.71588575e-01 -6.60777459e-02 8.81473243e-02 1.01913401e-01 -1.85250915e-03 1.75086272e-01 1.05300812e-01 1.06364295e-01 7.22972346e-02 2.60058145e-01 2.87998375e-01 1.93058929e-01 3.90019373e-01] [-1.41465715e-02 9.78615784e-02 -1.27031012e-01 2.12116393e-02 1.78985656e-02 -2.03918118e-01 -1.53711499e-01 5.31112061e-02 3.57861493e-01 -2.10819806e-01 -8.22258903e-02 -1.89785667e-01 3.45587879e-02 -3.64121660e-02 -1.11489699e-01 1.49562685e-01 2.59862784e-01 9.52931101e-02 -8.03868867e-02 -7.79103440e-02 -7.99559958e-02 -4.53007549e-02 -2.31401143e-02 -2.75176461e-02 -3.37378113e-02 -1.04769069e-01 -3.36737897e-02 -3.86974729e-02 -8.71500817e-02 3.38203890e-02 -1.83569476e-02 -4.88653591e-01 4.09923891e-02 -6.20894454e-02 -6.46112621e-02 3.82051988e-01 -1.34332696e-01 1.49243156e-02 -2.59092055e-02 1.45457468e-01 -2.00513017e-01 -2.99098945e-02 -4.37546854e-02 8.59870244e-02 4.44191485e-02 -2.07461786e-01] [-1.48544269e-01 1.51489601e-02 -7.62814671e-02 3.08627769e-01 1.75940802e-01 5.14423406e-02 4.71303119e-02 4.87007250e-02 -2.67637656e-01 1.74869971e-01 -1.29479824e-01 -1.70692228e-02 2.28283061e-01 2.90699601e-02 -8.39465659e-02 1.02403535e-01 1.76594601e-01 -3.45256285e-01 -4.77535216e-02 -2.11182720e-02 1.49663519e-01 -2.57271312e-01 8.13346820e-03 3.52417304e-02 3.37438979e-04 -9.35866931e-02 8.22987937e-03 2.80511149e-01 1.58291595e-01 1.48507228e-01 -3.96613670e-02 8.55423772e-02 -2.63169307e-02 -1.65307485e-01 -4.88475397e-02 8.18288543e-02 5.22455597e-02 -7.33381321e-02 -8.40647767e-02 3.65941124e-01 1.90773862e-02 5.14079486e-02 -4.39843393e-02 2.19081531e-01 1.51738794e-01 -2.75472643e-03]]
In [76]:
# Explore the importance of each feature for principle components
pca = PCA(n_components = 7).fit(chile_data_s_2)
vars = pca.explained_variance_ratio_
c_names = chile_data_s_2.columns
sum = 0
print('Variance: Projected dimension')
print('------------------------------')
for idx, row in enumerate(pca.components_):
output = '{0:4.1f}%: '.format(100.0 * vars[idx])
output += " + ".joi`n("{0:5.2f} * {1:s}".format(val, name) \
for val, name in zip(row, c_names))
sum += 100*vars[idx]
print(output)
print('Total variance explained by the 7 components {0:4.1f}%'.format(sum))
# Total variance explained by the 7 components 83.6.0%
Variance: Projected dimension ------------------------------ 38.2%: -0.13 * Density of restaurants and other food services per 100,000 inhabitants + -0.10 * Density of People employed in restaurants and the like per 10,000 inhabitants + 0.20 * Car rental companies + -0.01 * Densidad de camas en hospitales por cada 10.000 habitantes + 0.06 * Density of beds in hospitals per 10,000 inhabitants + 0.01 * Number of spas + -0.14 * Density of gambling casinos per million inhabitants + 0.22 * Number of golf courses + 0.06 * Number of craft centers + -0.10 * Density of tour guides per 100,000 inhabitants + 0.00 * Number of thermal centers + -0.02 * Density of Sports Facilities and Venues per 10,000 inhabitants + 0.10 * Penetration of telephone lines in service per 100 inhabitants + 0.23 * Density of service stations + 0.22 * Number of tour-operator companies certified with the tourism quality seal + 0.05 * Perception of exposure to crime (%) + 0.05 * Percentage of victimized households with at least one victim + -0.04 * Density of homicides per million inhabitants + -0.08 * Density of crimes against public health per million inhabitants + 0.05 * Black figure index + 0.22 * Budget for public safety (Thousands of $) + -0.03 * Percentage of households that reported at least one crime + 0.23 * Number of declared crimes + 0.23 * Number of crimes investigated + 0.23 * Number of accidents (roads, air and waterways) + 0.21 * Illegal commerce + 0.23 * Number of Carabineros + 0.11 * Unemployment rate + 0.02 * Poverty rate + -0.03 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + 0.23 * Number of strikes carried out + -0.03 * Average (days) duration of a strike + 0.21 * Person-day cost of a strike + 0.19 * Density of Bank Branches per million inhabitants + 0.22 * Floating population + 0.08 * Volume of exports + -0.13 * Density of Tourist Information Offices per million inhabitants + 0.17 * Number of visits to Tourist Information Offices + 0.21 * Average monthly global searches by tourist attraction on the internet + -0.06 * National tourism promotion budget (Thousands of USD) + -0.06 * International tourism promotion budget (Thousands of USD) + 0.22 * Investments in public infrastructure made by the Ministry of Public Works + 0.17 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.04 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + 0.19 * Funds obtained from FNRD (Thousands of pesos) + -0.03 * Number of regional strategic development plans 16.6%: 0.23 * Density of restaurants and other food services per 100,000 inhabitants + 0.23 * Density of People employed in restaurants and the like per 10,000 inhabitants + 0.07 * Car rental companies + 0.17 * Densidad de camas en hospitales por cada 10.000 habitantes + 0.26 * Density of beds in hospitals per 10,000 inhabitants + -0.20 * Number of spas + 0.26 * Density of gambling casinos per million inhabitants + 0.05 * Number of golf courses + 0.10 * Number of craft centers + 0.28 * Density of tour guides per 100,000 inhabitants + -0.19 * Number of thermal centers + -0.03 * Density of Sports Facilities and Venues per 10,000 inhabitants + 0.27 * Penetration of telephone lines in service per 100 inhabitants + -0.02 * Density of service stations + 0.09 * Number of tour-operator companies certified with the tourism quality seal + 0.06 * Perception of exposure to crime (%) + -0.05 * Percentage of victimized households with at least one victim + 0.06 * Density of homicides per million inhabitants + 0.17 * Density of crimes against public health per million inhabitants + 0.04 * Black figure index + -0.02 * Budget for public safety (Thousands of $) + -0.10 * Percentage of households that reported at least one crime + 0.04 * Number of declared crimes + 0.04 * Number of crimes investigated + 0.04 * Number of accidents (roads, air and waterways) + 0.13 * Illegal commerce + 0.01 * Number of Carabineros + -0.12 * Unemployment rate + -0.33 * Poverty rate + 0.04 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + 0.08 * Number of strikes carried out + -0.07 * Average (days) duration of a strike + 0.09 * Person-day cost of a strike + 0.04 * Density of Bank Branches per million inhabitants + 0.10 * Floating population + 0.06 * Volume of exports + 0.28 * Density of Tourist Information Offices per million inhabitants + 0.03 * Number of visits to Tourist Information Offices + 0.03 * Average monthly global searches by tourist attraction on the internet + -0.06 * National tourism promotion budget (Thousands of USD) + 0.30 * International tourism promotion budget (Thousands of USD) + -0.05 * Investments in public infrastructure made by the Ministry of Public Works + -0.06 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.15 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + -0.09 * Funds obtained from FNRD (Thousands of pesos) + -0.19 * Number of regional strategic development plans 8.8%: -0.07 * Density of restaurants and other food services per 100,000 inhabitants + -0.09 * Density of People employed in restaurants and the like per 10,000 inhabitants + -0.08 * Car rental companies + -0.22 * Densidad de camas en hospitales por cada 10.000 habitantes + 0.18 * Density of beds in hospitals per 10,000 inhabitants + -0.05 * Number of spas + -0.05 * Density of gambling casinos per million inhabitants + -0.00 * Number of golf courses + -0.24 * Number of craft centers + -0.07 * Density of tour guides per 100,000 inhabitants + -0.06 * Number of thermal centers + 0.28 * Density of Sports Facilities and Venues per 10,000 inhabitants + 0.07 * Penetration of telephone lines in service per 100 inhabitants + -0.07 * Density of service stations + -0.02 * Number of tour-operator companies certified with the tourism quality seal + 0.26 * Perception of exposure to crime (%) + 0.34 * Percentage of victimized households with at least one victim + -0.02 * Density of homicides per million inhabitants + -0.06 * Density of crimes against public health per million inhabitants + 0.35 * Black figure index + -0.08 * Budget for public safety (Thousands of $) + 0.01 * Percentage of households that reported at least one crime + -0.01 * Number of declared crimes + -0.06 * Number of crimes investigated + -0.03 * Number of accidents (roads, air and waterways) + 0.02 * Illegal commerce + -0.05 * Number of Carabineros + -0.05 * Unemployment rate + 0.01 * Poverty rate + 0.40 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + 0.02 * Number of strikes carried out + 0.11 * Average (days) duration of a strike + 0.18 * Person-day cost of a strike + -0.04 * Density of Bank Branches per million inhabitants + -0.01 * Floating population + 0.12 * Volume of exports + -0.04 * Density of Tourist Information Offices per million inhabitants + 0.15 * Number of visits to Tourist Information Offices + -0.04 * Average monthly global searches by tourist attraction on the internet + -0.28 * National tourism promotion budget (Thousands of USD) + -0.10 * International tourism promotion budget (Thousands of USD) + -0.10 * Investments in public infrastructure made by the Ministry of Public Works + -0.15 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.20 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + -0.13 * Funds obtained from FNRD (Thousands of pesos) + 0.05 * Number of regional strategic development plans 6.2%: 0.03 * Density of restaurants and other food services per 100,000 inhabitants + 0.02 * Density of People employed in restaurants and the like per 10,000 inhabitants + 0.17 * Car rental companies + 0.23 * Densidad de camas en hospitales por cada 10.000 habitantes + 0.21 * Density of beds in hospitals per 10,000 inhabitants + 0.22 * Number of spas + -0.04 * Density of gambling casinos per million inhabitants + -0.06 * Number of golf courses + -0.06 * Number of craft centers + -0.02 * Density of tour guides per 100,000 inhabitants + 0.03 * Number of thermal centers + -0.01 * Density of Sports Facilities and Venues per 10,000 inhabitants + 0.04 * Penetration of telephone lines in service per 100 inhabitants + -0.08 * Density of service stations + -0.05 * Number of tour-operator companies certified with the tourism quality seal + 0.31 * Perception of exposure to crime (%) + -0.11 * Percentage of victimized households with at least one victim + 0.08 * Density of homicides per million inhabitants + -0.22 * Density of crimes against public health per million inhabitants + -0.22 * Black figure index + 0.01 * Budget for public safety (Thousands of $) + 0.36 * Percentage of households that reported at least one crime + -0.05 * Number of declared crimes + -0.07 * Number of crimes investigated + -0.05 * Number of accidents (roads, air and waterways) + -0.09 * Illegal commerce + -0.07 * Number of Carabineros + 0.09 * Unemployment rate + -0.01 * Poverty rate + -0.24 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.02 * Number of strikes carried out + 0.24 * Average (days) duration of a strike + -0.05 * Person-day cost of a strike + -0.04 * Density of Bank Branches per million inhabitants + -0.04 * Floating population + 0.35 * Volume of exports + -0.05 * Density of Tourist Information Offices per million inhabitants + 0.31 * Number of visits to Tourist Information Offices + -0.03 * Average monthly global searches by tourist attraction on the internet + -0.01 * National tourism promotion budget (Thousands of USD) + 0.03 * International tourism promotion budget (Thousands of USD) + -0.11 * Investments in public infrastructure made by the Ministry of Public Works + 0.16 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.21 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + 0.01 * Funds obtained from FNRD (Thousands of pesos) + -0.06 * Number of regional strategic development plans 5.8%: 0.12 * Density of restaurants and other food services per 100,000 inhabitants + -0.05 * Density of People employed in restaurants and the like per 10,000 inhabitants + 0.06 * Car rental companies + -0.02 * Densidad de camas en hospitales por cada 10.000 habitantes + 0.05 * Density of beds in hospitals per 10,000 inhabitants + -0.06 * Number of spas + 0.11 * Density of gambling casinos per million inhabitants + -0.02 * Number of golf courses + -0.01 * Number of craft centers + 0.06 * Density of tour guides per 100,000 inhabitants + -0.01 * Number of thermal centers + 0.28 * Density of Sports Facilities and Venues per 10,000 inhabitants + -0.06 * Penetration of telephone lines in service per 100 inhabitants + -0.02 * Density of service stations + -0.07 * Number of tour-operator companies certified with the tourism quality seal + 0.21 * Perception of exposure to crime (%) + 0.10 * Percentage of victimized households with at least one victim + 0.40 * Density of homicides per million inhabitants + -0.17 * Density of crimes against public health per million inhabitants + -0.11 * Black figure index + 0.06 * Budget for public safety (Thousands of $) + -0.23 * Percentage of households that reported at least one crime + -0.02 * Number of declared crimes + 0.01 * Number of crimes investigated + -0.06 * Number of accidents (roads, air and waterways) + -0.06 * Illegal commerce + -0.01 * Number of Carabineros + -0.31 * Unemployment rate + 0.03 * Poverty rate + 0.08 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.05 * Number of strikes carried out + 0.00 * Average (days) duration of a strike + -0.03 * Person-day cost of a strike + 0.17 * Density of Bank Branches per million inhabitants + -0.07 * Floating population + 0.09 * Volume of exports + 0.10 * Density of Tourist Information Offices per million inhabitants + -0.00 * Number of visits to Tourist Information Offices + 0.18 * Average monthly global searches by tourist attraction on the internet + 0.11 * National tourism promotion budget (Thousands of USD) + 0.11 * International tourism promotion budget (Thousands of USD) + 0.07 * Investments in public infrastructure made by the Ministry of Public Works + 0.26 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + 0.29 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + 0.19 * Funds obtained from FNRD (Thousands of pesos) + 0.39 * Number of regional strategic development plans 5.0%: -0.01 * Density of restaurants and other food services per 100,000 inhabitants + 0.10 * Density of People employed in restaurants and the like per 10,000 inhabitants + -0.13 * Car rental companies + 0.02 * Densidad de camas en hospitales por cada 10.000 habitantes + 0.02 * Density of beds in hospitals per 10,000 inhabitants + -0.20 * Number of spas + -0.15 * Density of gambling casinos per million inhabitants + 0.05 * Number of golf courses + 0.36 * Number of craft centers + -0.21 * Density of tour guides per 100,000 inhabitants + -0.08 * Number of thermal centers + -0.19 * Density of Sports Facilities and Venues per 10,000 inhabitants + 0.03 * Penetration of telephone lines in service per 100 inhabitants + -0.04 * Density of service stations + -0.11 * Number of tour-operator companies certified with the tourism quality seal + 0.15 * Perception of exposure to crime (%) + 0.26 * Percentage of victimized households with at least one victim + 0.10 * Density of homicides per million inhabitants + -0.08 * Density of crimes against public health per million inhabitants + -0.08 * Black figure index + -0.08 * Budget for public safety (Thousands of $) + -0.05 * Percentage of households that reported at least one crime + -0.02 * Number of declared crimes + -0.03 * Number of crimes investigated + -0.03 * Number of accidents (roads, air and waterways) + -0.10 * Illegal commerce + -0.03 * Number of Carabineros + -0.04 * Unemployment rate + -0.09 * Poverty rate + 0.03 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.02 * Number of strikes carried out + -0.49 * Average (days) duration of a strike + 0.04 * Person-day cost of a strike + -0.06 * Density of Bank Branches per million inhabitants + -0.06 * Floating population + 0.38 * Volume of exports + -0.13 * Density of Tourist Information Offices per million inhabitants + 0.01 * Number of visits to Tourist Information Offices + -0.03 * Average monthly global searches by tourist attraction on the internet + 0.15 * National tourism promotion budget (Thousands of USD) + -0.20 * International tourism promotion budget (Thousands of USD) + -0.03 * Investments in public infrastructure made by the Ministry of Public Works + -0.04 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + 0.09 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + 0.04 * Funds obtained from FNRD (Thousands of pesos) + -0.21 * Number of regional strategic development plans 4.3%: -0.15 * Density of restaurants and other food services per 100,000 inhabitants + 0.02 * Density of People employed in restaurants and the like per 10,000 inhabitants + -0.08 * Car rental companies + 0.31 * Densidad de camas en hospitales por cada 10.000 habitantes + 0.18 * Density of beds in hospitals per 10,000 inhabitants + 0.05 * Number of spas + 0.05 * Density of gambling casinos per million inhabitants + 0.05 * Number of golf courses + -0.27 * Number of craft centers + 0.17 * Density of tour guides per 100,000 inhabitants + -0.13 * Number of thermal centers + -0.02 * Density of Sports Facilities and Venues per 10,000 inhabitants + 0.23 * Penetration of telephone lines in service per 100 inhabitants + 0.03 * Density of service stations + -0.08 * Number of tour-operator companies certified with the tourism quality seal + 0.10 * Perception of exposure to crime (%) + 0.18 * Percentage of victimized households with at least one victim + -0.35 * Density of homicides per million inhabitants + -0.05 * Density of crimes against public health per million inhabitants + -0.02 * Black figure index + 0.15 * Budget for public safety (Thousands of $) + -0.26 * Percentage of households that reported at least one crime + 0.01 * Number of declared crimes + 0.04 * Number of crimes investigated + 0.00 * Number of accidents (roads, air and waterways) + -0.09 * Illegal commerce + 0.01 * Number of Carabineros + 0.28 * Unemployment rate + 0.16 * Poverty rate + 0.15 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.04 * Number of strikes carried out + 0.09 * Average (days) duration of a strike + -0.03 * Person-day cost of a strike + -0.17 * Density of Bank Branches per million inhabitants + -0.05 * Floating population + 0.08 * Volume of exports + 0.05 * Density of Tourist Information Offices per million inhabitants + -0.07 * Number of visits to Tourist Information Offices + -0.08 * Average monthly global searches by tourist attraction on the internet + 0.37 * National tourism promotion budget (Thousands of USD) + 0.02 * International tourism promotion budget (Thousands of USD) + 0.05 * Investments in public infrastructure made by the Ministry of Public Works + -0.04 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + 0.22 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + 0.15 * Funds obtained from FNRD (Thousands of pesos) + -0.00 * Number of regional strategic development plans Total variance explained by the 7 components 84.8%
In [79]:
pd.DataFrame(np.column_stack((chile_data_s_2.columns, pca.components_[0]))).sort_values(by = 1, ascending = False)
#above information, for the Principal Component 1 (sorted)
Out[79]:
| 0 | 1 | |
|---|---|---|
| 22 | Number of declared crimes | 0.234363 |
| 26 | Number of Carabineros | 0.233118 |
| 24 | Number of accidents (roads, air and waterways) | 0.232392 |
| 23 | Number of crimes investigated | 0.230352 |
| 13 | Density of service stations | 0.229039 |
| 30 | Number of strikes carried out | 0.227837 |
| 34 | Floating population | 0.224281 |
| 41 | Investments in public infrastructure made by t... | 0.220136 |
| 7 | Number of golf courses | 0.219424 |
| 20 | Budget for public safety (Thousands of $) | 0.218917 |
| 14 | Number of tour-operator companies certified wi... | 0.217724 |
| 38 | Average monthly global searches by tourist att... | 0.212374 |
| 25 | Illegal commerce | 0.208387 |
| 32 | Person-day cost of a strike | 0.20688 |
| 2 | Car rental companies | 0.195812 |
| 44 | Funds obtained from FNRD (Thousands of pesos) | 0.193036 |
| 33 | Density of Bank Branches per million inhabitants | 0.190525 |
| 37 | Number of visits to Tourist Information Offices | 0.168545 |
| 42 | Investment Initiatives in projects or programs... | 0.168277 |
| 27 | Unemployment rate | 0.108428 |
| 12 | Penetration of telephone lines in service per ... | 0.104726 |
| 35 | Volume of exports | 0.0820219 |
| 8 | Number of craft centers | 0.0581024 |
| 4 | Density of beds in hospitals per 10,000 inhabi... | 0.0576553 |
| 16 | Percentage of victimized households with at le... | 0.0503569 |
| 19 | Black figure index | 0.0470673 |
| 15 | Perception of exposure to crime (%) | 0.0468836 |
| 28 | Poverty rate | 0.0185849 |
| 5 | Number of spas | 0.00668292 |
| 10 | Number of thermal centers | 0.00189879 |
| 3 | Densidad de camas en hospitales por cada 10.00... | -0.0100633 |
| 11 | Density of Sports Facilities and Venues per 10... | -0.0170435 |
| 29 | Density of crimes against property law and ind... | -0.0262714 |
| 31 | Average (days) duration of a strike | -0.0271694 |
| 45 | Number of regional strategic development plans | -0.0288824 |
| 21 | Percentage of households that reported at leas... | -0.0348171 |
| 43 | Contributions of government funds to the Touri... | -0.0369478 |
| 17 | Density of homicides per million inhabitants | -0.0416695 |
| 39 | National tourism promotion budget (Thousands o... | -0.0644263 |
| 40 | International tourism promotion budget (Thousa... | -0.0644487 |
| 18 | Density of crimes against public health per mi... | -0.0776543 |
| 1 | Density of People employed in restaurants and ... | -0.0971941 |
| 9 | Density of tour guides per 100,000 inhabitants | -0.0980069 |
| 36 | Density of Tourist Information Offices per mil... | -0.126775 |
| 0 | Density of restaurants and other food services... | -0.133132 |
| 6 | Density of gambling casinos per million inhabi... | -0.139482 |
In [80]:
# Calculate factor scores
pca_model = myPCA.fit_transform(chile_data_s_2)
PCcomponents = pd.DataFrame(data = pca_model, columns = ['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7'])
print("\n The Factor scores are")
PCcomponents
The Factor scores are
Out[80]:
| PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | |
|---|---|---|---|---|---|---|---|
| 0 | -2.373870 | 0.823256 | 0.472329 | -2.079397 | -2.919301 | 0.141580 | 0.186033 |
| 1 | -1.485061 | -0.247090 | 6.228244 | -2.115655 | 1.203540 | 0.595868 | 0.757801 |
| 2 | 0.263871 | 2.084163 | 1.119329 | 4.384186 | 0.480078 | 3.195863 | 0.422146 |
| 3 | -1.814625 | 0.019734 | 2.139679 | 2.229493 | -0.772417 | -1.927273 | -0.772461 |
| 4 | -0.969405 | -1.998904 | -0.011820 | 0.458845 | 1.718456 | -1.177366 | -1.155089 |
| 5 | 1.188020 | 0.340730 | -0.711721 | -0.291174 | -0.750611 | 1.368108 | 0.495793 |
| 6 | 14.460293 | 2.855751 | 0.166888 | -0.525576 | -0.548933 | -0.744969 | -0.624666 |
| 7 | -1.283626 | -0.937935 | -0.789281 | -1.051599 | -2.088445 | 1.379180 | -1.114501 |
| 8 | -0.680583 | -3.431526 | -0.366983 | -0.213786 | -1.694869 | -0.554999 | -0.381959 |
| 9 | 2.024578 | -3.385638 | -1.595265 | -1.373500 | 2.088322 | 0.846775 | 3.060953 |
| 10 | 0.412162 | -4.107222 | -0.063790 | 1.987826 | -0.289221 | -2.512417 | 0.329350 |
| 11 | -1.888205 | -1.361590 | -2.279907 | 0.261054 | -1.076617 | 1.509792 | -0.109209 |
| 12 | 0.031550 | -0.800397 | -1.746730 | -0.594429 | 3.044853 | -0.001577 | -1.242050 |
| 13 | -3.898966 | 3.945054 | -0.990212 | -1.273527 | 1.729195 | 0.068358 | -2.559529 |
| 14 | -3.986135 | 6.201613 | -1.570759 | 0.197240 | -0.124031 | -2.186923 | 2.707388 |
In [82]:
# Example of different variables in each component
#visualize an example how variables can contribute to diffent principal components
# Fit the model
myPCA = PCA(n_components = 7)
pca_model = myPCA.fit(chile_data_s_2)
y_axis = [0,0,0,0,0,0,0]
for i in range(0,7):
y_axis[i]=[np.mean(pca_model.components_[i][0:15]), np.mean(pca_model.components_[i][15:27]),
np.mean(pca_model.components_[i][27:36]), np.mean(pca_model.components_[i][36:41]),
np.mean(pca_model.components_[i][41:46])]
# Plot
x_axis = ['TOURISM-RELATED SERVICES', 'SECURITY AND SAFETY ', 'ECONOMIC PERFORMANCE', 'TOURISM PROMOTION', 'GOVERNMENTAL INVOLVEMENT AND EFFICIENCY']
plt.plot(x_axis,y_axis[0], color = 'mediumaquamarine', label = "C1")
plt.plot(x_axis,y_axis[1], color = 'yellow', label = "C2")
plt.plot(x_axis,y_axis[2], color = 'pink', label = "C3")
plt.plot(x_axis,y_axis[3], color = 'steelblue', label = "C4")
plt.plot(x_axis,y_axis[4], color = 'salmon', label = "C5")
plt.plot(x_axis,y_axis[5], color = 'red', label = "C6")
plt.plot(x_axis,y_axis[6], color = 'orange', label = "C7")
plt.xticks(rotation = 90)
plt.title('Example of variable contributions to each principal component')
plt.legend()
pass
3. Developing a scoring system for 5 dimensions
Step 1 - Calculate a weighted average for each variable in principal components.
Multiply the percentage value of the explained variance by the percentage value of a feature in the selected principal component. As a result, a weighted average will be a new column in the dataframe with principal components.
In [83]:
# Creating a dataframe of weights
weights = pd.DataFrame(np.column_stack((chile_data_s_2.columns, pca_model.components_[0] *
pca_model.explained_variance_ratio_[0],
pca_model.components_[1] * pca_model.explained_variance_ratio_[1],
pca_model.components_[2] * pca_model.explained_variance_ratio_[2],
pca_model.components_[3] * pca_model.explained_variance_ratio_[3],
pca_model.components_[4] * pca_model.explained_variance_ratio_[4],
pca_model.components_[5] * pca_model.explained_variance_ratio_[5],
pca_model.components_[6] * pca_model.explained_variance_ratio_[6])))
weights = weights.set_index(0)
# Create a weighted average
weights['weighted_average'] = weights.sum(axis = 1)/np.sum(pca_model.explained_variance_ratio_)
# Print
weights.head()
Out[83]:
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | weighted_average | |
|---|---|---|---|---|---|---|---|---|
| 0 | ||||||||
| Density of restaurants and other food services per 100,000 inhabitants | -0.0508779 | 0.0383434 | -0.00603016 | 0.00173496 | 0.00703431 | -0.000705732 | -0.00638432 | -0.019904 |
| Density of People employed in restaurants and the like per 10,000 inhabitants | -0.0371438 | 0.0379041 | -0.00768655 | 0.00145828 | -0.00263994 | 0.00488203 | 0.000651091 | -0.003035 |
| Car rental companies | 0.0748317 | 0.0115487 | -0.0072556 | 0.0104844 | 0.00355798 | -0.00633721 | -0.00327852 | 0.098485 |
| Densidad de camas en hospitales por cada 10.000 habitantes | -0.00384581 | 0.0285705 | -0.0193489 | 0.0143164 | -0.00104324 | 0.00105819 | 0.0132646 | 0.038865 |
| Density of beds in hospitals per 10,000 inhabitants | 0.0220336 | 0.0438976 | 0.0153522 | 0.0128237 | 0.00260291 | 0.000892908 | 0.0075618 | 0.123961 |
Step 2. Calculate a score for each dimension.
Multiply weighted average of a variable by each standartized value in each column and sum up results, receiving a final score.
In [84]:
# Example
chile_data_s_2.head(1)
Out[84]:
| Density of restaurants and other food services per 100,000 inhabitants | Density of People employed in restaurants and the like per 10,000 inhabitants | Car rental companies | Densidad de camas en hospitales por cada 10.000 habitantes | Density of beds in hospitals per 10,000 inhabitants | Number of spas | Density of gambling casinos per million inhabitants | Number of golf courses | Number of craft centers | Density of tour guides per 100,000 inhabitants | Number of thermal centers | Density of Sports Facilities and Venues per 10,000 inhabitants | Penetration of telephone lines in service per 100 inhabitants | Density of service stations | Number of tour-operator companies certified with the tourism quality seal | Perception of exposure to crime (%) | Percentage of victimized households with at least one victim | Density of homicides per million inhabitants | Density of crimes against public health per million inhabitants | Black figure index | Budget for public safety (Thousands of $) | Percentage of households that reported at least one crime | Number of declared crimes | Number of crimes investigated | Number of accidents (roads, air and waterways) | Illegal commerce | Number of Carabineros | Unemployment rate | Poverty rate | Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants | Number of strikes carried out | Average (days) duration of a strike | Person-day cost of a strike | Density of Bank Branches per million inhabitants | Floating population | Volume of exports | Density of Tourist Information Offices per million inhabitants | Number of visits to Tourist Information Offices | Average monthly global searches by tourist attraction on the internet | National tourism promotion budget (Thousands of USD) | International tourism promotion budget (Thousands of USD) | Investments in public infrastructure made by the Ministry of Public Works | Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) | Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population | Funds obtained from FNRD (Thousands of pesos) | Number of regional strategic development plans | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | ||||||||||||||||||||||||||||||||||||||||||||||
| Arica y Parinacota | 0.087073 | -0.79801 | -0.411459 | -0.820845 | -0.633616 | -0.60701 | 0.342739 | -0.560316 | -0.554658 | -0.135893 | 0.057354 | -1.871499 | 0.695488 | -0.749525 | 0.121472 | -0.248797 | -0.093065 | -1.087756 | 2.859839 | 1.648551 | -0.996415 | -0.65277 | -0.512578 | -0.565722 | -0.513953 | -0.258745 | -0.663 | 0.812831 | -0.834653 | 0.204305 | -0.433573 | -0.533574 | -0.597159 | -0.448859 | -0.392595 | -0.771859 | 0.417721 | -0.484381 | -0.554876 | 0.288149 | -0.067919 | -0.731979 | -1.298074 | -0.291691 | -1.15157 | -0.953463 |
As a result, we multiply:
weighted averageforDensity of restaurants and other food services per 100,000 inhabitants(0.087073) in the dataframeweightsby each value in theDensity of restaurants and other food services per 100,000 inhabitantscolumn in the dataframechile_data_s_2.Do the same for all weighted averages and columns in respective dataframe
Sum up the product of multiplications and receive the score for the first dimension
In [85]:
# Ranking for dimension 6
# Create a dataframe for relevant variables
dim6 = chile_data_s_2.iloc[:, 0:15].mul(weights['weighted_average'][0:15], axis = 1)
# Create a score ranking
dim6['Ranking 6'] = dim6.sum(axis = 1)
# Sort by score
dim6.sort_values(by = 'Ranking 6', ascending = False).head()
Out[85]:
| Density of restaurants and other food services per 100,000 inhabitants | Density of People employed in restaurants and the like per 10,000 inhabitants | Car rental companies | Densidad de camas en hospitales por cada 10.000 habitantes | Density of beds in hospitals per 10,000 inhabitants | Number of spas | Density of gambling casinos per million inhabitants | Number of golf courses | Number of craft centers | Density of tour guides per 100,000 inhabitants | Number of thermal centers | Density of Sports Facilities and Venues per 10,000 inhabitants | Penetration of telephone lines in service per 100 inhabitants | Density of service stations | Number of tour-operator companies certified with the tourism quality seal | Ranking 6 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | ||||||||||||||||
| Metropolitana | 0.027651 | 0.003034 | 0.291889 | 0.001947 | 0.163672 | 0.023149 | 0.022439 | 0.344744 | 0.024562 | -0.000959 | 0.012079 | -0.009206 | 0.259132 | 0.278443 | 0.351165 | 1.793741 |
| Antofagasta | -0.003268 | -0.001249 | 0.054452 | 0.040697 | 0.205288 | 0.003858 | 0.000265 | -0.020279 | 0.014184 | -0.000202 | 0.042275 | -0.010101 | 0.119666 | -0.048206 | -0.029492 | 0.367888 |
| Valparaíso | -0.019993 | -0.003931 | -0.002533 | 0.021514 | 0.056133 | -0.034723 | 0.011771 | 0.162232 | 0.028021 | -0.000602 | 0.027177 | -0.000799 | 0.064596 | 0.049509 | -0.019204 | 0.339170 |
| Magallanes y Antártica | -0.027274 | -0.006771 | -0.040523 | 0.081745 | 0.231735 | 0.023149 | -0.050179 | -0.060837 | -0.022140 | 0.005464 | 0.072472 | -0.001523 | 0.192925 | -0.060770 | -0.019204 | 0.318268 |
| Biobío | 0.022239 | 0.003707 | -0.002533 | 0.016326 | -0.140644 | 0.010288 | 0.013611 | 0.040558 | -0.003113 | -0.001116 | 0.027177 | 0.003036 | -0.066064 | 0.071844 | -0.039780 | -0.044464 |
In [86]:
# Ranking for dimension 7
# Create a dataframe for relevant variables
dim7 = chile_data_s_2.iloc[:, 15:27].mul(weights['weighted_average'][15:27], axis = 1)
# Create a score ranking
dim7['Ranking 7'] = dim7.sum(axis = 1)
# Sort the by score
dim7.sort_values(by = 'Ranking 7', ascending = False).head()
Out[86]:
| Perception of exposure to crime (%) | Percentage of victimized households with at least one victim | Density of homicides per million inhabitants | Density of crimes against public health per million inhabitants | Black figure index | Budget for public safety (Thousands of $) | Percentage of households that reported at least one crime | Number of declared crimes | Number of crimes investigated | Number of accidents (roads, air and waterways) | Illegal commerce | Number of Carabineros | Ranking 7 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | |||||||||||||
| Metropolitana | 0.047892 | 0.029820 | -0.005519 | 0.017258 | 0.031286 | 0.289907 | 0.026840 | 0.384044 | 0.340181 | 0.350464 | 0.365357 | 0.330985 | 2.208516 |
| Biobío | 0.031120 | 0.045423 | -0.005922 | 0.023287 | 0.001409 | 0.137902 | 0.040228 | 0.049240 | 0.074065 | 0.034232 | -0.040694 | 0.063199 | 0.453489 |
| Tarapacá | 0.116375 | 0.209779 | 0.000929 | 0.023287 | 0.058774 | -0.071726 | 0.051703 | -0.035178 | -0.060643 | -0.048898 | -0.010863 | -0.049287 | 0.184253 |
| Valparaíso | -0.037363 | 0.025659 | 0.000868 | 0.005492 | 0.008579 | 0.002698 | 0.006758 | 0.027251 | 0.073708 | 0.053211 | -0.033773 | 0.044516 | 0.177603 |
| Antofagasta | 0.291078 | 0.049584 | 0.007057 | 0.023287 | -0.021896 | -0.011193 | -0.041057 | -0.017101 | -0.042283 | -0.026907 | -0.042304 | -0.041806 | 0.126460 |
In [87]:
# Ranking for dimension 8
# Create a dataframe for relevant variables
dim8 = chile_data_s_2.iloc[:, 27:36].mul(weights['weighted_average'][27:36], axis = 1)
# Create a score ranking
dim8['Ranking 8'] = dim8.sum(axis = 1)
# Sort the dataframe by score
dim8.sort_values(by = 'Ranking 8', ascending = False).head()
Out[87]:
| Unemployment rate | Poverty rate | Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants | Number of strikes carried out | Average (days) duration of a strike | Person-day cost of a strike | Density of Bank Branches per million inhabitants | Floating population | Volume of exports | Ranking 8 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Region | ||||||||||
| Metropolitana | 0.020622 | 0.034039 | -0.006377 | 0.418184 | 0.007745 | 0.419765 | 0.250552 | 0.386336 | 0.087579 | 1.618444 |
| Antofagasta | -0.000859 | 0.054850 | -0.014695 | 0.006476 | 0.014940 | 0.026760 | -0.018619 | -0.030050 | 0.397214 | 0.436016 |
| Tarapacá | -0.020909 | -0.029784 | 0.128731 | -0.025905 | 0.000817 | 0.125953 | -0.028268 | -0.039164 | 0.000041 | 0.111514 |
| Valparaíso | 0.017758 | -0.004810 | -0.003026 | -0.007401 | 0.012009 | -0.018861 | -0.026963 | 0.047685 | 0.031123 | 0.047514 |
| Los Lagos | -0.039526 | -0.007585 | -0.011188 | -0.044409 | 0.019203 | -0.067512 | 0.178138 | -0.036873 | -0.029088 | -0.038840 |
In [88]:
# Ranking for dimension 9
# Create a dataframe for relevant variables
dim9 = chile_data_s_2.iloc[:, 36:41].mul(weights['weighted_average'][36:41], axis = 1)
# Create a score ranking
dim9['Ranking 9'] = dim9.sum(axis = 1)
# Sort the dataframe by score
dim9.sort_values(by = 'Ranking 9', ascending = False).head()
Out[88]:
| Density of Tourist Information Offices per million inhabitants | Number of visits to Tourist Information Offices | Average monthly global searches by tourist attraction on the internet | National tourism promotion budget (Thousands of USD) | International tourism promotion budget (Thousands of USD) | Ranking 9 | |
|---|---|---|---|---|---|---|
| Region | ||||||
| Metropolitana | 0.007732 | 0.297038 | 0.315846 | 0.051934 | -1.807070e-18 | 0.672550 |
| Antofagasta | -0.000633 | 0.212435 | -0.003504 | -0.013374 | 2.520136e-03 | 0.197444 |
| Coquimbo | 0.004745 | 0.068566 | 0.070512 | -0.008762 | -6.927919e-03 | 0.128133 |
| Atacama | -0.000370 | 0.083933 | -0.070812 | 0.034985 | 7.243356e-03 | 0.054979 |
| Araucanía | 0.005887 | 0.025383 | -0.033017 | 0.028272 | -1.256697e-02 | 0.013958 |
In [89]:
# Ranking for dimension 10
# Create a dataframe for relevant variables
dim10 = chile_data_s_2.iloc[:, 41:46].mul(weights['weighted_average'][41:46], axis = 1)
# Create a score ranking
dim10['Ranking 10'] = dim10.sum(axis = 1)
# Sort the dataframe by score
dim10.sort_values(by = 'Ranking 10', ascending = False).head()
Out[89]:
| Investments in public infrastructure made by the Ministry of Public Works | Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) | Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population | Funds obtained from FNRD (Thousands of pesos) | Number of regional strategic development plans | Ranking 10 | |
|---|---|---|---|---|---|---|
| Region | ||||||
| Metropolitana | 0.231341 | 0.146845 | 0.062107 | 0.172525 | 0.034371 | 0.647190 |
| Los Lagos | 0.039541 | 0.126108 | -0.023540 | 0.060047 | -0.017186 | 0.184970 |
| Biobío | 0.094131 | 0.030705 | -0.138642 | 0.161778 | -0.068743 | 0.079230 |
| Antofagasta | -0.053708 | 0.037057 | 0.045566 | 0.013379 | 0.034371 | 0.076666 |
| Valparaíso | 0.040477 | 0.000473 | -0.032905 | 0.016581 | 0.034371 | 0.058999 |
In [90]:
# Create an aggregated dataframe with all scores
scoring_data2 = pd.concat([dim6.iloc[:,-1:], dim7.iloc[:,-1:], dim8.iloc[:,-1:], dim9.iloc[:,-1:],
dim10.iloc[:,-1:]], axis = 1)
scoring_data2
Out[90]:
| Ranking 6 | Ranking 7 | Ranking 8 | Ranking 9 | Ranking 10 | |
|---|---|---|---|---|---|
| Region | |||||
| Arica y Parinacota | -0.219562 | -0.431607 | -0.218964 | -0.127449 | -0.193358 |
| Tarapacá | -0.139428 | 0.184253 | 0.111514 | -0.033586 | -0.196716 |
| Antofagasta | 0.367888 | 0.126460 | 0.436016 | 0.197444 | 0.076666 |
| Atacama | -0.059748 | -0.323617 | -0.149980 | 0.054979 | -0.156984 |
| Coquimbo | -0.327888 | -0.296003 | -0.271570 | 0.128133 | -0.040475 |
| Valparaíso | 0.339170 | 0.177603 | 0.047514 | -0.061269 | 0.058999 |
| Metropolitana | 1.793741 | 2.208516 | 1.618444 | 0.672550 | 0.647190 |
| O'Higgins | -0.319237 | -0.415101 | -0.064561 | -0.184920 | -0.053559 |
| Maule | -0.494721 | -0.279228 | -0.305912 | -0.114380 | -0.006079 |
| Biobío | -0.044464 | 0.453489 | -0.071235 | -0.087292 | 0.079230 |
| Araucanía | -0.402073 | 0.026774 | -0.323501 | 0.013958 | 0.052154 |
| Los Ríos | -0.381948 | -0.423620 | -0.316565 | -0.131099 | -0.070488 |
| Los Lagos | -0.127404 | -0.209206 | -0.038840 | -0.032185 | 0.184970 |
| Aysén | -0.302594 | -0.322888 | -0.210162 | -0.088698 | -0.261150 |
| Magallanes y Antártica | 0.318268 | -0.475825 | -0.242200 | -0.206185 | -0.120398 |
In [91]:
scoring_data2.style.highlight_null().render().split('\n')[:10]
def color_negative_red(val):
"""
Takes a scalar and returns a string with
the css property `'color: red'` for negative
strings, black otherwise.
"""
color = 'red' if val < 0 else 'black'
return 'color: %s' % color
def highlight_max(s):
'''
highlight the maximum in a Series yellow.
'''
is_max = s == s.max()
return ['background-color: yellow' if v else '' for v in is_max]
scoring_data2.style.\
applymap(color_negative_red).\
apply(highlight_max)
Out[91]:
| Ranking 6 | Ranking 7 | Ranking 8 | Ranking 9 | Ranking 10 | |
|---|---|---|---|---|---|
| Region | |||||
| Arica y Parinacota | -0.219562 | -0.431607 | -0.218964 | -0.127449 | -0.193358 |
| Tarapacá | -0.139428 | 0.184253 | 0.111514 | -0.033586 | -0.196716 |
| Antofagasta | 0.367888 | 0.126460 | 0.436016 | 0.197444 | 0.076666 |
| Atacama | -0.059748 | -0.323617 | -0.149980 | 0.054979 | -0.156984 |
| Coquimbo | -0.327888 | -0.296003 | -0.271570 | 0.128133 | -0.040475 |
| Valparaíso | 0.339170 | 0.177603 | 0.047514 | -0.061269 | 0.058999 |
| Metropolitana | 1.793741 | 2.208516 | 1.618444 | 0.672550 | 0.647190 |
| O'Higgins | -0.319237 | -0.415101 | -0.064561 | -0.184920 | -0.053559 |
| Maule | -0.494721 | -0.279228 | -0.305912 | -0.114380 | -0.006079 |
| Biobío | -0.044464 | 0.453489 | -0.071235 | -0.087292 | 0.079230 |
| Araucanía | -0.402073 | 0.026774 | -0.323501 | 0.013958 | 0.052154 |
| Los Ríos | -0.381948 | -0.423620 | -0.316565 | -0.131099 | -0.070488 |
| Los Lagos | -0.127404 | -0.209206 | -0.038840 | -0.032185 | 0.184970 |
| Aysén | -0.302594 | -0.322888 | -0.210162 | -0.088698 | -0.261150 |
| Magallanes y Antártica | 0.318268 | -0.475825 | -0.242200 | -0.206185 | -0.120398 |
4. PCA for 10 dimensions
In [92]:
chile_data_2 = chile_data_2.drop(['Region'], axis = 1)
In [93]:
# First, we need to combine two dataframes with 5 dimensions each into one
chile_data = pd.concat([chile_data_1.reset_index(drop = True), chile_data_2], axis = 1)
# Print
chile_data
Out[93]:
| Region | CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR | NUMBER OF CULTURAL CENTERS | WORLD CULTURAL HERITAGE SITES | NUMBER OF ARCHEOLOGICAL SITES | NATIONAL MONUMENTS | MUSEUMS | % OF POPULATION THAT ATTENDS MUSEUMS | THEATERS | NUMBER OF THEATER PLAYS PER YEAR | LIBRARIES | GALERIES | % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP | NUMBER OF EXHIBITS | ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR | MAJOR SPORTS EVENTS PER YEAR | OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS | ARTWORK SITES | POPULAR ARCHITECTURE SITES | HISTORICAL SITES | LOCAL MARKETS | CULTURAL SITES LEVEL III (INTERNATIONAL) | CULTURA SITES LEVEL II (NATIONAL) | CULTURAL SITES LEVEL I (LOCAL) | HERITAGE ARCHITECTURAL HOUSES | % OF LAND THAT CORRESPONDS TO FORESTS | NATIONAL PROTECTED SITES (%) | % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS | TOXIC WASTE DISPOSAL (TONS/100 hab.) | NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED | ENVIRONMENTAL ISSUES PER MILLION HABITANTS | NUMBER OF BEACHES AND BEACH RESORTS | LAND AFFECTED BY WILDFIRES | NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) | NATURAL PROTECTED SITES LEVEL II (NATIONAL) | RIVERS, LAKES AND WATERFALLS | MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS | GEISERS AND THERMAL CENTERS | PIERS AND SEASHORES | GLACIERS AND WINTER VACATION LOCATIONS | VALLEYS | DESERTS AND DUNES | ISLANDS AND PENINSULAS | PALEONTOLOGY SITES | HIKING TRAILS | PRESERVED SITES | SEASHORE PROTECTED SITES | BIOSHPERE RESERVES | % AVAILABLE WORKFORCE | % POPULATION ORIENTED TOWARDS TOURISM | AVERAGE MONTHLY INCOME (CHILEAN PESOS) | 5 POPULATION WITH PRIMARY EDUCATION | % POPULATION WITH SECONDARY EDUCATION | AVERAGE NUMBER OF YEARS STUDYING | HIGHER EDUCATION AND TECHNICAL INSTITUTIONS | TOURISM-ORIENTED INSTITUTIONS | NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS | AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS | DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) | CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS | % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR | ROOMS PER 1000 HABITANTS | NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. | TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) | AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR | AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND | NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION | NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS | TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS | TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) | NATIONAL TOURISTS ARRIVALS | INTERNATIONAL TOURISTS ARRIVALS | NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY | DENSITY OF AIRPORTS | DENSITY OF ROADS AND HIGHWAYS | % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) | NUMBER OF VEHICLES | VISITORS TO PROTECTED SITES | NUMBER OF CRUISES THAT ARRIVE PER YEAR | TOURIST'S ARRIVALS THROUGH BORDER LINES | SECONDARY ROADS (KMS) | NUMBER OF INTERNATIONAL BORDER GATES | Density of restaurants and other food services per 100,000 inhabitants | Density of People employed in restaurants and the like per 10,000 inhabitants | Car rental companies | Densidad de camas en hospitales por cada 10.000 habitantes | Density of beds in hospitals per 10,000 inhabitants | Number of spas | Density of gambling casinos per million inhabitants | Number of golf courses | Number of craft centers | Density of tour guides per 100,000 inhabitants | Number of thermal centers | Density of Sports Facilities and Venues per 10,000 inhabitants | Penetration of telephone lines in service per 100 inhabitants | Density of service stations | Number of tour-operator companies certified with the tourism quality seal | Perception of exposure to crime (%) | Percentage of victimized households with at least one victim | Density of homicides per million inhabitants | Density of crimes against public health per million inhabitants | Black figure index | Budget for public safety (Thousands of $) | Percentage of households that reported at least one crime | Number of declared crimes | Number of crimes investigated | Number of accidents (roads, air and waterways) | Illegal commerce | Number of Carabineros | Unemployment rate | Poverty rate | Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants | Number of strikes carried out | Average (days) duration of a strike | Person-day cost of a strike | Density of Bank Branches per million inhabitants | Floating population | Volume of exports | Density of Tourist Information Offices per million inhabitants | Number of visits to Tourist Information Offices | Average monthly global searches by tourist attraction on the internet | National tourism promotion budget (Thousands of USD) | International tourism promotion budget (Thousands of USD) | Investments in public infrastructure made by the Ministry of Public Works | Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) | Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population | Funds obtained from FNRD (Thousands of pesos) | Number of regional strategic development plans | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Arica y Parinacota | 31.0 | 2 | 0 | 30 | 28 | 4 | 22.1 | 2 | 105 | 12 | 2 | 25.4 | 1 | 0 | 0 | 0 | 4 | 32 | 4 | 3 | 9 | 32 | 7 | 59 | 21.291667 | 21.9 | 0.46 | 58.00 | 1 | 0.00 | 5 | 1.138182 | 13 | 13 | 4 | 6 | 6 | 2 | 0 | 8 | 2 | 0 | 0 | 2 | 5.230769 | 0 | 1 | 42.556 | 0.88 | 293648 | 94.1 | 83.8 | 11.1 | 53.0 | 2 | 193 | 11 | 20.038 | 356 | 23.69 | 18.74 | 6544 | 33.01 | 37.0 | 2.3 | 5 | 3 | 0.0 | 0 | 97454 | 34186 | 1.151575e+06 | 5.2730 | 2.129 | 17.35 | 45248 | 15045 | 13 | 167211 | 4.67 | 3 | 35.857 | 19.563 | 12.0 | 21.619 | 43.766 | 0.0 | 5.273 | 1.0 | 20.0 | 20.038 | 5.0 | 0.000 | 240.55 | 17.0 | 6.0 | 0.318 | 0.260 | 42.184 | 15.819 | 0.513000 | 11884613.0 | 0.426 | 26467.0 | 2387.0 | 981.0 | 319.000000 | 265.0 | 7.3 | 8.2 | 84.369 | 1.0 | 11.0 | 253.0 | 94.915 | 45560.0 | 137.0 | 5.273 | 6517.0 | 1060490.0 | 193364.0 | 118413.000000 | 36370835.0 | 11757543.0 | 446922.0 | 24729573.0 | 1.0 |
| 1 | Tarapacá | 0.0 | 5 | 1 | 13 | 73 | 5 | 20.8 | 2 | 178 | 12 | 1 | 12.6 | 1 | 0 | 0 | 0 | 0 | 34 | 10 | 1 | 6 | 34 | 12 | 0 | 0.200000 | 9.1 | 0.03 | 76.03 | 1 | 0.00 | 16 | 1.138182 | 2 | 6 | 1 | 1 | 7 | 5 | 0 | 5 | 6 | 4 | 2 | 3 | 5.000000 | 0 | 0 | 68.563 | 1.45 | 381466 | 91.7 | 66.7 | 10.7 | 42.0 | 5 | 255 | 19 | 22.180 | 380 | 23.03 | 22.17 | 11108 | 41.43 | 42.8 | 2.1 | 5 | 10 | 0.0 | 11 | 235365 | 40919 | 1.956000e+04 | 4.1850 | 4.021 | 15.90 | 81182 | 17161 | 0 | 434727 | 184.10 | 1 | 25.947 | 26.616 | 2.0 | 18.958 | 60.264 | 0.0 | 4.185 | 3.0 | 9.0 | 22.180 | 5.0 | 40.134 | 213.07 | 28.0 | 0.0 | 0.421 | 0.468 | 87.884 | 0.000 | 0.516000 | 14740693.0 | 0.398 | 76896.0 | 1862.0 | 1105.0 | 593.000000 | 592.0 | 4.8 | 13.4 | 485.457 | 6.0 | 14.9 | 59745.0 | 138.104 | 83265.0 | 3236.0 | 4.185 | 7600.0 | 1750481.0 | 74404.0 | 13600.000000 | 42364100.0 | 12796789.0 | 355208.0 | 29714382.0 | 2.0 |
| 2 | Antofagasta | 1.0 | 9 | 0 | 28 | 81 | 16 | 27.4 | 8 | 203 | 15 | 2 | 5.7 | 2 | 1 | 0 | 3 | 1 | 24 | 37 | 0 | 4 | 31 | 26 | 63 | 21.291667 | 2.8 | 0.03 | 39.56 | 2 | 0.00 | 22 | 1.138182 | 5 | 28 | 8 | 13 | 3 | 14 | 1 | 6 | 10 | 6 | 4 | 0 | 5.000000 | 0 | 0 | 54.486 | 1.08 | 475866 | 91.7 | 66.7 | 10.6 | 40.0 | 7 | 529 | 47 | 20.446 | 184 | 24.55 | 22.76 | 19920 | 35.02 | 44.6 | 1.8 | 2 | 10 | 17.0 | 15 | 413922 | 84195 | 2.289800e+04 | 2.0244 | 3.332 | 52.25 | 112607 | 315888 | 0 | 115100 | 23.00 | 5 | 37.248 | 35.446 | 22.0 | 29.960 | 69.233 | 3.0 | 4.049 | 3.0 | 36.0 | 20.446 | 2.0 | 14.292 | 259.23 | 39.0 | 2.0 | 0.546 | 0.314 | 109.315 | 0.000 | 0.381000 | 22383612.0 | 0.495 | 123017.0 | 4228.0 | 2324.0 | 7.000000 | 765.0 | 6.2 | 7.3 | 12.146 | 13.0 | 9.6 | 30220.0 | 178.143 | 238010.0 | 16666.0 | 4.049 | 16280.0 | 2846340.0 | 200000.0 | 149000.000000 | 38262875.0 | 29066423.0 | 192572.0 | 50841622.0 | 1.0 |
| 3 | Atacama | 8.0 | 10 | 0 | 8 | 35 | 7 | 20.0 | 0 | 144 | 12 | 0 | 7.5 | 0 | 0 | 0 | 0 | 0 | 18 | 5 | 0 | 2 | 29 | 7 | 33 | 21.291667 | 2.0 | 0.02 | 40.76 | 0 | 3.93 | 17 | 1.138182 | 7 | 23 | 2 | 7 | 2 | 10 | 0 | 3 | 13 | 1 | 3 | 2 | 5.000000 | 1 | 0 | 51.844 | 0.94 | 379971 | 89.1 | 73.2 | 10.3 | 47.0 | 1 | 108 | 16 | 18.479 | 578 | 19.43 | 18.49 | 9674 | 29.25 | 36.0 | 1.9 | 1 | 3 | 0.0 | 15 | 168508 | 14222 | 2.416000e+03 | 0.0000 | 3.635 | 24.39 | 55812 | 24873 | 0 | 2552 | 11.33 | 2 | 44.823 | 26.933 | 19.0 | 25.478 | 56.225 | 1.0 | 3.932 | 1.0 | 9.0 | 18.479 | 3.0 | 32.437 | 184.01 | 35.0 | 1.0 | 0.390 | 0.197 | 66.841 | 0.000 | 0.488000 | 17068077.0 | 0.533 | 21705.0 | 3125.0 | 965.0 | 14.000000 | 504.0 | 6.4 | 10.2 | 15.727 | 3.0 | 29.3 | 17860.0 | 145.477 | 53046.0 | 3456.0 | 3.932 | 11614.0 | 556668.0 | 94100.0 | 187035.000000 | 35948639.0 | 16986593.0 | 233225.0 | 30224699.0 | 2.0 |
| 4 | Coquimbo | 23.0 | 7 | 0 | 2 | 52 | 7 | 18.3 | 4 | 80 | 22 | 2 | 1.7 | 0 | 1 | 0 | 7 | 1 | 69 | 2 | 1 | 0 | 29 | 27 | 97 | 0.800000 | 0.4 | 0.35 | 25.45 | 1 | 0.00 | 37 | 1.500000 | 4 | 16 | 7 | 0 | 1 | 21 | 0 | 18 | 0 | 9 | 1 | 0 | 5.000000 | 2 | 1 | 56.498 | 0.92 | 338014 | 92.7 | 71.2 | 9.7 | 43.0 | 7 | 870 | 85 | 6.963 | 1074 | 15.50 | 15.63 | 24346 | 12.00 | 35.2 | 2.8 | 4 | 9 | 383.9 | 750 | 205850 | 25803 | 2.786000e+03 | 0.0000 | 1.912 | 283.37 | 116263 | 57234 | 0 | 15265 | 228.58 | 1 | 37.632 | 25.165 | 9.0 | 17.805 | 47.745 | 3.0 | 3.316 | 3.0 | 13.0 | 6.963 | 3.0 | 14.091 | 150.06 | 72.0 | 3.0 | 0.288 | 0.218 | 155.833 | 0.000 | 0.396000 | 20225710.0 | 0.449 | 69974.0 | 4203.0 | 1831.0 | 100.000000 | 1082.0 | 6.0 | 12.3 | 11.605 | 3.0 | 29.0 | 1431.0 | 112.730 | 275447.0 | 2229.0 | 1.658 | 11056.0 | 5364222.0 | 189900.0 | 72917.000000 | 64917630.0 | 33222779.0 | 673800.0 | 44790979.0 | 3.0 |
| 5 | Valparaíso | 14.0 | 37 | 2 | 24 | 161 | 12 | 25.7 | 7 | 322 | 56 | 7 | 3.2 | 3 | 3 | 1 | 4 | 2 | 48 | 21 | 4 | 46 | 90 | 36 | 720 | 6.500000 | 2.7 | 2.11 | 38.46 | 13 | 0.00 | 59 | 6.640000 | 13 | 71 | 3 | 0 | 3 | 14 | 3 | 22 | 1 | 0 | 0 | 14 | 5.000000 | 3 | 2 | 53.534 | 0.99 | 311264 | 92.9 | 73.5 | 10.6 | 27.0 | 21 | 3949 | 316 | 12.209 | 3661 | 17.21 | 13.39 | 44504 | 21.18 | 29.7 | 2.0 | 14 | 27 | 920.0 | 257 | 430436 | 106915 | 3.173090e+05 | 0.6494 | 0.197 | 280.95 | 316618 | 146161 | 31 | 436195 | 93.86 | 1 | 52.408 | 47.050 | 16.0 | 27.756 | 55.850 | 9.0 | 1.948 | 12.0 | 44.0 | 12.209 | 3.0 | 18.710 | 230.65 | 179.0 | 3.0 | 0.311 | 0.291 | 87.671 | 1.948 | 0.432000 | 24137492.0 | 0.445 | 236177.0 | 19176.0 | 6765.0 | 166.000000 | 2761.0 | 7.5 | 11.6 | 50.654 | 10.0 | 10.7 | 16641.0 | 143.520 | 1557887.0 | 4287.0 | 1.299 | 5649.0 | 3058423.0 | 129481.0 | 63996.000000 | 101802434.0 | 24239428.0 | 811772.0 | 51647041.0 | 1.0 |
| 6 | Metropolitana | 4.0 | 56 | 0 | 1 | 404 | 35 | 22.5 | 70 | 6558 | 127 | 25 | 4.1 | 30 | 0 | 0 | 3 | 2 | 5 | 9 | 4 | 4 | 85 | 112 | 274 | 6.900000 | 0.9 | 7.25 | 47.03 | 16 | 0.00 | 0 | 0.040000 | 3 | 3 | 0 | 3 | 4 | 0 | 5 | 9 | 0 | 0 | 0 | 0 | 5.000000 | 0 | 0 | 55.901 | 6.06 | 421484 | 93.1 | 70.5 | 11.2 | 19.0 | 30 | 12881 | 1261 | 4.834 | 15221 | 38.13 | 4.33 | 43634 | 9.32 | 59.8 | 2.2 | 15 | 133 | 754.2 | 236 | 448887 | 765681 | 1.091111e+06 | 1.2810 | 0.731 | 541.32 | 1306140 | 83459 | 0 | 1147039 | 248.50 | 1 | 9.223 | 16.914 | 47.0 | 25.508 | 65.499 | 0.0 | 0.000 | 21.0 | 42.0 | 4.834 | 4.0 | 14.717 | 331.61 | 507.0 | 39.0 | 0.372 | 0.295 | 65.334 | 0.660 | 0.470000 | 60400444.0 | 0.424 | 1146510.0 | 53517.0 | 23242.0 | 7605.000000 | 9385.0 | 7.7 | 8.8 | 39.596 | 102.0 | 12.3 | 147198.0 | 1295.113 | 7307884.0 | 6196.0 | 0.330 | 19352.0 | 13709951.0 | 56982.0 | 128705.928571 | 230562951.0 | 43552270.0 | 62052.0 | 90875903.0 | 1.0 |
| 7 | O'Higgins | 32.0 | 13 | 1 | 9 | 67 | 6 | 21.9 | 13 | 251 | 36 | 0 | 1.9 | 1 | 7 | 2 | 3 | 16 | 29 | 3 | 1 | 0 | 28 | 15 | 229 | 11.300000 | 2.8 | 0.92 | 27.15 | 4 | 1.28 | 12 | 2.470000 | 5 | 22 | 1 | 3 | 2 | 2 | 7 | 5 | 2 | 1 | 1 | 7 | 5.000000 | 0 | 0 | 54.662 | 0.62 | 308068 | 95.5 | 65.0 | 9.5 | 23.0 | 7 | 352 | 15 | 9.992 | 2833 | 17.46 | 8.01 | 14526 | 6.34 | 15.4 | 2.2 | 2 | 7 | 10.0 | 0 | 73478 | 7066 | 2.405341e+05 | 0.0000 | 1.352 | 134.50 | 164204 | 11073 | 0 | 0 | 62.12 | 0 | 18.959 | 49.422 | 6.0 | 19.946 | 43.427 | 0.0 | 2.562 | 3.0 | 39.0 | 9.992 | 2.0 | 14.950 | 117.30 | 114.0 | 3.0 | 0.203 | 0.220 | 51.241 | 0.000 | 0.396000 | 18182163.0 | 0.477 | 111020.0 | 4894.0 | 2451.0 | 762.000000 | 1136.0 | 6.0 | 9.9 | 11.529 | 7.0 | 10.2 | 26831.0 | 95.805 | 265601.0 | 3056.0 | 1.281 | 4700.0 | 754000.0 | 179000.0 | 6000.000000 | 50200910.0 | 17850727.0 | 444491.0 | 43045390.0 | 1.0 |
| 8 | Maule | 29.0 | 17 | 0 | 1 | 54 | 7 | 11.1 | 9 | 657 | 29 | 1 | 1.6 | 1 | 7 | 0 | 0 | 10 | 27 | 4 | 3 | 0 | 37 | 28 | 19 | 12.700000 | 0.6 | 0.41 | 32.86 | 5 | 0.00 | 18 | 0.820000 | 0 | 20 | 23 | 12 | 7 | 5 | 6 | 13 | 3 | 0 | 0 | 9 | 5.000000 | 0 | 0 | 48.164 | 0.24 | 244231 | 93.4 | 73.1 | 9.0 | 24.0 | 4 | 364 | 37 | 8.700 | 2898 | 14.49 | 6.10 | 12278 | 8.67 | 28.4 | 2.0 | 3 | 14 | 0.0 | 0 | 167293 | 8935 | 3.942000e+03 | 0.0000 | 1.480 | 197.65 | 185728 | 64500 | 0 | 853 | 232.90 | 2 | 15.637 | 19.943 | 10.0 | 23.852 | 35.899 | 9.0 | 1.101 | 1.0 | 24.0 | 8.700 | 5.0 | 25.383 | 109.73 | 130.0 | 0.0 | 0.245 | 0.227 | 27.530 | 1.101 | 0.427000 | 25936948.0 | 0.530 | 124820.0 | 6018.0 | 4771.0 | 194.000000 | 1875.0 | 6.4 | 15.8 | 11.012 | 5.0 | 10.7 | 1371.0 | 48.346 | 165417.0 | 1311.0 | 1.101 | 7183.0 | 647510.0 | 168850.0 | 8000.000000 | 97730159.0 | 19292650.0 | 486500.0 | 48826193.0 | 2.0 |
| 9 | Biobío | 5.0 | 32 | 0 | 0 | 59 | 20 | 18.9 | 12 | 488 | 63 | 7 | 3.9 | 10 | 0 | 0 | 0 | 4 | 13 | 23 | 2 | 0 | 10 | 54 | 135 | 20.700000 | 2.8 | 0.96 | 31.68 | 2 | 1.07 | 25 | 0.730000 | 3 | 7 | 19 | 3 | 2 | 4 | 3 | 2 | 0 | 1 | 8 | 0 | 5.000000 | 0 | 0 | 48.651 | 1.23 | 290367 | 92.7 | 71.1 | 9.9 | 23.0 | 17 | 4023 | 320 | 1.612 | 2334 | 16.04 | 4.93 | 20802 | 7.89 | 36.0 | 1.9 | 5 | 12 | 25.0 | 9 | 393481 | 31409 | 1.546000e+03 | 0.0000 | 1.021 | 400.89 | 312085 | 49841 | 0 | 828 | 642.26 | 1 | 14.128 | 14.004 | 16.0 | 27.160 | 38.194 | 2.0 | 1.612 | 6.0 | 26.0 | 1.612 | 3.0 | 20.531 | 162.84 | 211.0 | 1.0 | 0.360 | 0.310 | 63.925 | 0.000 | 0.420000 | 41208340.0 | 0.410 | 292281.0 | 19222.0 | 5713.0 | 37.000000 | 3193.0 | 7.9 | 15.8 | 12.892 | 12.0 | 11.8 | 15518.0 | 188.607 | 305920.0 | 4475.0 | 1.074 | 7362.0 | 3548528.0 | 317000.0 | 44000.000000 | 137998425.0 | 28228357.0 | 1646127.0 | 88172439.0 | 3.0 |
| 10 | Araucanía | 18.0 | 13 | 0 | 2 | 96 | 5 | 13.2 | 8 | 179 | 58 | 4 | 30.1 | 0 | 5 | 3 | 0 | 13 | 16 | 6 | 10 | 0 | 12 | 31 | 20 | 29.400000 | 9.6 | 0.43 | 36.79 | 6 | 0.00 | 25 | 0.160000 | 22 | 79 | 51 | 10 | 17 | 3 | 7 | 10 | 0 | 1 | 3 | 23 | 5.000000 | 0 | 1 | 52.282 | 2.30 | 251081 | 94.5 | 72.0 | 9.1 | 29.0 | 8 | 709 | 67 | 9.085 | 709 | 11.21 | 9.90 | 26140 | 16.09 | 37.4 | 2.5 | 15 | 7 | 24.0 | 25 | 200377 | 38524 | 1.307130e+05 | 0.0000 | 1.615 | 236.97 | 124956 | 228921 | 0 | 112246 | 469.74 | 3 | 15.986 | 15.687 | 25.0 | 25.887 | 40.826 | 23.0 | 2.300 | 2.0 | 16.0 | 9.085 | 13.0 | 22.736 | 135.88 | 118.0 | 5.0 | 0.381 | 0.288 | 78.203 | 1.150 | 0.369000 | 29000528.0 | 0.469 | 140063.0 | 8685.0 | 3161.0 | 323.000000 | 1550.0 | 8.0 | 18.1 | 20.701 | 4.0 | 30.3 | 5856.0 | 232.887 | 395583.0 | 372.0 | 1.150 | 9488.0 | 1842375.0 | 108799.0 | 27507.000000 | 67212297.0 | 31050589.0 | 552127.5 | 54529735.0 | 2.0 |
| 11 | Los Ríos | 3.0 | 9 | 0 | 1 | 33 | 8 | 16.2 | 3 | 94 | 12 | 2 | 16.7 | 2 | 1 | 0 | 1 | 0 | 10 | 5 | 2 | 0 | 10 | 19 | 55 | 46.100000 | 6.9 | 0.31 | 35.41 | 0 | 0.00 | 10 | 0.140000 | 2 | 31 | 13 | 3 | 14 | 4 | 0 | 1 | 0 | 5 | 1 | 0 | 5.230769 | 0 | 1 | 47.218 | 0.41 | 268648 | 93.6 | 64.8 | 9.3 | 31.0 | 4 | 444 | 58 | 8.698 | 868 | 17.28 | 18.91 | 12846 | 11.81 | 37.9 | 1.7 | 9 | 1 | 54.1 | 0 | 113900 | 16070 | 1.114500e+04 | 0.0000 | 2.019 | 133.02 | 51811 | 376 | 7 | 0 | 42.67 | 1 | 34.512 | 26.207 | 9.0 | 30.472 | 42.649 | 2.0 | 2.806 | 1.0 | 40.0 | 8.698 | 12.0 | 0.000 | 191.32 | 44.0 | 1.0 | 0.274 | 0.270 | 101.011 | 0.000 | 0.298000 | 15834434.0 | 0.440 | 22076.0 | 3235.0 | 818.0 | 29.000000 | 773.0 | 6.4 | 14.3 | 11.223 | 4.0 | 11.3 | 6171.0 | 51.623 | 71200.0 | 71.0 | 2.806 | 7109.0 | 1476220.0 | 253292.0 | 35000.000000 | 49376288.0 | 22065484.0 | 486875.0 | 32409855.0 | 1.0 |
| 12 | Los Lagos | 1.0 | 24 | 1 | 0 | 64 | 11 | 19.8 | 8 | 272 | 31 | 1 | 20.8 | 2 | 0 | 0 | 0 | 0 | 38 | 3 | 3 | 0 | 57 | 30 | 94 | 56.300000 | 15.9 | 0.18 | 34.77 | 1 | 0.00 | 18 | 0.000000 | 12 | 41 | 17 | 10 | 9 | 10 | 3 | 6 | 0 | 31 | 0 | 0 | 7.000000 | 3 | 1 | 54.555 | 14.97 | 291431 | 92.9 | 66.7 | 9.1 | 31.0 | 10 | 856 | 72 | 20.928 | 2672 | 14.08 | 23.97 | 35548 | 23.82 | 31.6 | 1.8 | 27 | 13 | 54.1 | 48 | 275043 | 105048 | 2.594110e+05 | 10.9299 | 1.893 | 135.48 | 129882 | 486725 | 28 | 192977 | 660.16 | 3 | 36.415 | 27.360 | 24.0 | 22.644 | 47.298 | 2.0 | 4.186 | 2.0 | 27.0 | 20.928 | 8.0 | 25.728 | 162.58 | 86.0 | 6.0 | 0.323 | 0.201 | 104.641 | 0.000 | 0.374000 | 19901362.0 | 0.426 | 85949.0 | 8729.0 | 2023.0 | 192.000000 | 1409.0 | 3.5 | 11.8 | 23.719 | 2.0 | 8.0 | 2160.0 | 994.622 | 122157.0 | 2251.0 | 4.186 | 4708.0 | 7108761.0 | 264511.0 | 96915.000000 | 101170739.0 | 40816121.0 | 737875.0 | 62581190.0 | 2.0 |
| 13 | Aysén | 0.0 | 4 | 0 | 2 | 17 | 0 | 14.8 | 0 | 49 | 12 | 0 | 21.8 | 0 | 0 | 0 | 0 | 0 | 24 | 2 | 0 | 1 | 20 | 10 | 0 | 44.400000 | 39.4 | 0.02 | 43.89 | 0 | 0.00 | 0 | 0.010000 | 21 | 93 | 25 | 8 | 6 | 5 | 23 | 3 | 0 | 8 | 3 | 6 | 6.000000 | 0 | 2 | 61.016 | 1.05 | 418044 | 95.3 | 68.5 | 9.5 | 66.0 | 3 | 64 | 6 | 67.765 | 83 | 6.83 | 37.60 | 8020 | 23.06 | 21.7 | 1.7 | 0 | 1 | 0.0 | 19 | 28584 | 10763 | 9.087600e+04 | 0.0000 | 4.089 | 3.70 | 19348 | 32030 | 0 | 56201 | 5.00 | 4 | 76.509 | 40.987 | 15.0 | 27.543 | 43.720 | 0.0 | 10.930 | 0.0 | 57.0 | 67.765 | 4.0 | 24.264 | 170.81 | 20.0 | 0.0 | 0.351 | 0.256 | 174.879 | 10.930 | 0.417643 | 15972532.0 | 0.445 | 14119.0 | 3349.0 | 422.0 | 795.461538 | 469.0 | 4.4 | 5.2 | 32.790 | 1.0 | 7.0 | 1953.0 | 106.083 | 13667.0 | 379.0 | 10.930 | 5660.0 | 200654.0 | 65000.0 | 454500.000000 | 32558406.0 | 13692259.0 | 794614.0 | 29462726.0 | 2.0 |
| 14 | Magallanes y Antártica | 0.0 | 3 | 0 | 2 | 45 | 6 | 27.7 | 2 | 4 | 14 | 0 | 22.7 | 3 | 0 | 0 | 0 | 2 | 16 | 16 | 0 | 2 | 24 | 11 | 34 | 20.200000 | 57.3 | 0.04 | 40.58 | 3 | 0.00 | 0 | 0.010000 | 19 | 62 | 23 | 10 | 0 | 17 | 20 | 10 | 0 | 0 | 1 | 1 | 5.000000 | 1 | 1 | 51.872 | 0.06 | 572203 | 93.6 | 77.6 | 10.2 | 60.0 | 6 | 380 | 55 | 137.244 | 222 | 12.94 | 41.53 | 12436 | 67.76 | 37.1 | 2.0 | 5 | 3 | 0.0 | 12 | 84034 | 109092 | 2.621900e+05 | 0.1650 | 3.632 | 6.02 | 46336 | 283629 | 18 | 388598 | 0.00 | 5 | 59.008 | 59.340 | 12.0 | 34.676 | 71.606 | 0.0 | 13.260 | 1.0 | 15.0 | 137.244 | 0.0 | 18.366 | 297.25 | 21.0 | 3.0 | 0.283 | 0.180 | 53.041 | 6.630 | 0.367000 | 20075842.0 | 0.414 | 8659.0 | 2527.0 | 660.0 | 795.461538 | 216.0 | 5.4 | 6.1 | 86.192 | 1.0 | 22.0 | 616.0 | 5.115 | 322800.0 | 397.0 | 13.260 | 4217.0 | 558678.0 | 266000.0 | 525000.000000 | 30955753.0 | 18036736.0 | 357752.0 | 30288105.0 | 1.0 |
In [94]:
# Dimensions
chile_data.shape
Out[94]:
(15, 128)
In [95]:
# Standardize data for applying PCA
# Create a copy
chile_data_s = chile_data.copy()
# Standardize
scaler = StandardScaler()
chile_data_s.loc[:, chile_data_s.columns != 'Region'] = scaler.fit_transform(chile_data_s.loc[:,
chile_data_s.columns != 'Region'])
# Set region as an index column
chile_data_s = chile_data_s.set_index('Region')
In [96]:
# Print
chile_data_s
# We can see that data was standardized
Out[96]:
| CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR | NUMBER OF CULTURAL CENTERS | WORLD CULTURAL HERITAGE SITES | NUMBER OF ARCHEOLOGICAL SITES | NATIONAL MONUMENTS | MUSEUMS | % OF POPULATION THAT ATTENDS MUSEUMS | THEATERS | NUMBER OF THEATER PLAYS PER YEAR | LIBRARIES | GALERIES | % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP | NUMBER OF EXHIBITS | ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR | MAJOR SPORTS EVENTS PER YEAR | OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS | ARTWORK SITES | POPULAR ARCHITECTURE SITES | HISTORICAL SITES | LOCAL MARKETS | CULTURAL SITES LEVEL III (INTERNATIONAL) | CULTURA SITES LEVEL II (NATIONAL) | CULTURAL SITES LEVEL I (LOCAL) | HERITAGE ARCHITECTURAL HOUSES | % OF LAND THAT CORRESPONDS TO FORESTS | NATIONAL PROTECTED SITES (%) | % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS | TOXIC WASTE DISPOSAL (TONS/100 hab.) | NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED | ENVIRONMENTAL ISSUES PER MILLION HABITANTS | NUMBER OF BEACHES AND BEACH RESORTS | LAND AFFECTED BY WILDFIRES | NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) | NATURAL PROTECTED SITES LEVEL II (NATIONAL) | RIVERS, LAKES AND WATERFALLS | MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS | GEISERS AND THERMAL CENTERS | PIERS AND SEASHORES | GLACIERS AND WINTER VACATION LOCATIONS | VALLEYS | DESERTS AND DUNES | ISLANDS AND PENINSULAS | PALEONTOLOGY SITES | HIKING TRAILS | PRESERVED SITES | SEASHORE PROTECTED SITES | BIOSHPERE RESERVES | % AVAILABLE WORKFORCE | % POPULATION ORIENTED TOWARDS TOURISM | AVERAGE MONTHLY INCOME (CHILEAN PESOS) | 5 POPULATION WITH PRIMARY EDUCATION | % POPULATION WITH SECONDARY EDUCATION | AVERAGE NUMBER OF YEARS STUDYING | HIGHER EDUCATION AND TECHNICAL INSTITUTIONS | TOURISM-ORIENTED INSTITUTIONS | NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS | AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS | DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) | CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS | % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR | ROOMS PER 1000 HABITANTS | NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. | TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) | AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR | AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND | NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION | NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS | TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS | TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) | NATIONAL TOURISTS ARRIVALS | INTERNATIONAL TOURISTS ARRIVALS | NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY | DENSITY OF AIRPORTS | DENSITY OF ROADS AND HIGHWAYS | % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) | NUMBER OF VEHICLES | VISITORS TO PROTECTED SITES | NUMBER OF CRUISES THAT ARRIVE PER YEAR | TOURIST'S ARRIVALS THROUGH BORDER LINES | SECONDARY ROADS (KMS) | NUMBER OF INTERNATIONAL BORDER GATES | Density of restaurants and other food services per 100,000 inhabitants | Density of People employed in restaurants and the like per 10,000 inhabitants | Car rental companies | Densidad de camas en hospitales por cada 10.000 habitantes | Density of beds in hospitals per 10,000 inhabitants | Number of spas | Density of gambling casinos per million inhabitants | Number of golf courses | Number of craft centers | Density of tour guides per 100,000 inhabitants | Number of thermal centers | Density of Sports Facilities and Venues per 10,000 inhabitants | Penetration of telephone lines in service per 100 inhabitants | Density of service stations | Number of tour-operator companies certified with the tourism quality seal | Perception of exposure to crime (%) | Percentage of victimized households with at least one victim | Density of homicides per million inhabitants | Density of crimes against public health per million inhabitants | Black figure index | Budget for public safety (Thousands of $) | Percentage of households that reported at least one crime | Number of declared crimes | Number of crimes investigated | Number of accidents (roads, air and waterways) | Illegal commerce | Number of Carabineros | Unemployment rate | Poverty rate | Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants | Number of strikes carried out | Average (days) duration of a strike | Person-day cost of a strike | Density of Bank Branches per million inhabitants | Floating population | Volume of exports | Density of Tourist Information Offices per million inhabitants | Number of visits to Tourist Information Offices | Average monthly global searches by tourist attraction on the internet | National tourism promotion budget (Thousands of USD) | International tourism promotion budget (Thousands of USD) | Investments in public infrastructure made by the Ministry of Public Works | Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) | Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population | Funds obtained from FNRD (Thousands of pesos) | Number of regional strategic development plans | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Arica y Parinacota | 1.672984 | -0.959349 | -0.559017 | 2.121142 | -0.617597 | -0.721316 | 0.440365 | -0.475143 | -0.335687 | -0.724558 | -0.261024 | 1.389477 | -0.368577 | -0.668153 | -0.454859 | -0.679900 | 0.067176 | 0.326242 | -0.616670 | 0.294619 | 0.360486 | -0.136165 | -0.834058 | -0.355859 | 0.000000 | 0.643982 | -0.248227 | 1.426648 | -0.576151 | -0.410550 | -0.836955 | 0.000000 | 0.597479 | -0.772105 | -0.685892 | 0.015686 | 0.100452 | -0.947748 | -0.755610 | -0.011653 | -0.118729 | -0.580615 | -0.855528 | -0.386889 | 1.657385e-15 | -0.620174 | 0.476731 | -1.851488 | -0.362785 | -0.632189 | 0.646530 | 2.619732 | 1.554178 | 1.133129 | -0.884585 | -0.477620 | -0.477498 | -0.135893 | -0.524853 | 0.790598 | 0.091521 | -1.132711 | 0.620125 | 0.166320 | 0.816497 | -0.347974 | -0.437516 | -0.521005 | -0.482777 | -0.920689 | -0.322261 | 2.514051e+00 | 1.231771 | -0.062345 | -0.946158 | -0.519001 | -0.762949 | 0.621100 | -0.126593 | -0.862524 | 0.528271 | 0.087073 | -0.798010 | -0.411459 | -0.820845 | -0.633616 | -0.607010 | 0.342739 | -0.560316 | -0.554658 | -0.135893 | 0.057354 | -1.871499 | 0.695488 | -0.749525 | 0.121472 | -0.248797 | -0.093065 | -1.087756 | 2.859839 | 1.648551 | -0.996415 | -0.652770 | -0.512578 | -0.565722 | -0.513953 | -2.587452e-01 | -0.663000 | 0.812831 | -0.834653 | 0.204305 | -0.433573 | -0.533574 | -0.597159 | -0.448859 | -0.392595 | -0.771859 | 0.417721 | -0.484381 | -0.554876 | 0.288149 | -6.791934e-02 | -0.731979 | -1.298074 | -0.291691 | -1.151570 | -0.953463 |
| Tarapacá | -0.955183 | -0.754748 | 1.118034 | 0.467040 | -0.126575 | -0.599746 | 0.164252 | -0.475143 | -0.289792 | -0.724558 | -0.424164 | 0.064193 | -0.368577 | -0.668153 | -0.454859 | -0.679900 | -0.738938 | 0.453349 | 0.000000 | -0.508888 | 0.094554 | -0.051062 | -0.638575 | -0.688420 | -1.306936 | -0.162045 | -0.490079 | 2.901672 | -0.576151 | -0.410550 | -0.106280 | 0.000000 | -0.942896 | -1.025452 | -0.911185 | -1.160793 | 0.315706 | -0.451833 | -0.755610 | -0.536045 | 0.898951 | -0.060661 | 0.095059 | -0.230042 | -4.306269e-01 | -0.620174 | -0.953463 | 2.567121 | -0.207694 | 0.357940 | -0.936809 | -0.869163 | 0.995791 | 0.344242 | -0.494327 | -0.458377 | -0.451687 | -0.072248 | -0.518277 | 0.696840 | 0.413159 | -0.752896 | 1.147544 | 0.759346 | 0.136083 | -0.347974 | -0.216654 | -0.521005 | -0.425756 | 0.095203 | -0.285528 | -6.097864e-01 | 0.863508 | 1.513222 | -0.955497 | -0.401928 | -0.747731 | -0.614762 | 0.777892 | -0.044781 | -0.792406 | -0.462238 | -0.260925 | -1.375815 | -1.416782 | 0.849682 | -0.607010 | 0.025557 | -0.186772 | -1.336868 | -0.072248 | 0.057354 | 2.063212 | 0.255363 | -0.658989 | -0.521615 | 1.049823 | 2.963394 | 0.083237 | -0.549390 | 1.700415 | -0.757515 | -1.353955 | -0.328183 | -0.606462 | -0.491469 | -1.099479e-01 | -0.515181 | -1.141089 | 0.586808 | 3.657754 | -0.229057 | -0.038898 | 1.017526 | -0.328069 | -0.371529 | 0.000349 | 0.115821 | -0.228403 | -0.353907 | -1.225117 | -7.595427e-01 | -0.616910 | -1.189453 | -0.545976 | -0.899207 | 0.476731 |
| Antofagasta | -0.870404 | -0.481948 | -0.559017 | 1.926542 | -0.039282 | 0.737525 | 1.566057 | -0.112746 | -0.274074 | -0.626053 | -0.261024 | -0.650217 | -0.233732 | -0.267261 | -0.454859 | 0.777029 | -0.537409 | -0.182187 | 2.775014 | -0.910642 | -0.082735 | -0.178716 | -0.091225 | -0.333313 | 0.000000 | -0.558761 | -0.490079 | -0.081919 | -0.360095 | -0.410550 | 0.292270 | 0.000000 | -0.522794 | -0.229219 | -0.385501 | 1.662758 | -0.545311 | 1.035911 | -0.610300 | -0.361247 | 1.916631 | 0.199315 | 1.045645 | -0.700582 | -4.306269e-01 | -0.620174 | -0.953463 | 0.175428 | -0.308367 | 1.422279 | -0.936809 | -0.869163 | 0.856194 | 0.200808 | -0.234155 | -0.373331 | -0.361350 | -0.123770 | -0.571981 | 0.912767 | 0.468484 | -0.019562 | 0.746029 | 0.943388 | -0.884538 | -0.771186 | -0.216654 | -0.461755 | -0.405021 | 1.410506 | -0.049429 | -6.005751e-01 | 0.132194 | 0.939456 | -0.721374 | -0.299545 | 1.400719 | -0.614762 | -0.302782 | -0.778986 | 1.848947 | 0.164176 | 0.411478 | 0.552897 | 1.047141 | 1.656064 | -0.101168 | -0.014090 | -0.186772 | 0.583102 | -0.123770 | -0.802955 | -0.470321 | 0.994671 | -0.568454 | -0.307253 | 2.625818 | 0.700439 | 0.632374 | -0.549390 | -0.633488 | -0.118212 | 1.075151 | -0.159541 | -0.422858 | -0.270437 | -4.281786e-01 | -0.436977 | -0.046894 | -1.080675 | -0.417550 | 0.057264 | -0.711150 | 0.216182 | -0.216088 | -0.285070 | 3.346832 | 0.078083 | 1.823205 | -0.034723 | 0.372564 | 1.339133e-01 | -0.695652 | 0.511042 | -0.996899 | 0.170390 | -0.953463 |
| Atacama | -0.276947 | -0.413748 | -0.559017 | -0.019460 | -0.541216 | -0.356606 | -0.005664 | -0.595941 | -0.311168 | -0.724558 | -0.587304 | -0.463849 | -0.503423 | -0.668153 | -0.454859 | -0.679900 | -0.738938 | -0.563509 | -0.513892 | -0.910642 | -0.260023 | -0.263819 | -0.834058 | -0.502412 | 0.000000 | -0.609138 | -0.495703 | 0.016253 | -0.792208 | 3.443259 | -0.039855 | 0.000000 | -0.242726 | -0.410181 | -0.836088 | 0.250982 | -0.760565 | 0.374691 | -0.755610 | -0.885639 | 2.679891 | -0.450626 | 0.570352 | -0.386889 | -4.306269e-01 | 0.310087 | -0.953463 | -0.273450 | -0.346460 | 0.341084 | -2.652094 | 0.457025 | 0.437403 | 0.702827 | -1.014671 | -0.504003 | -0.461366 | -0.182215 | -0.464025 | 0.185433 | 0.068078 | -0.872233 | 0.384602 | 0.064074 | -0.544331 | -0.912257 | -0.437516 | -0.521005 | -0.405021 | -0.397285 | -0.431178 | -6.570959e-01 | -0.553020 | 1.191780 | -0.900815 | -0.484583 | -0.692266 | -0.614762 | -0.683313 | -0.832171 | -0.132068 | 0.584058 | -0.236785 | 0.263591 | 0.043387 | 0.486545 | -0.438396 | -0.048199 | -0.560316 | -1.336868 | -0.182215 | -0.516185 | 1.308603 | -0.210069 | -0.601376 | -0.414434 | 0.658976 | -1.018820 | -0.455958 | -0.549390 | 1.216346 | -0.562837 | 2.026759 | -0.529991 | -0.508452 | -0.516855 | -4.243772e-01 | -0.554961 | 0.109420 | -0.287937 | -0.386717 | -0.351766 | 1.787599 | -0.119284 | -0.307448 | -0.388412 | 0.055168 | 0.045618 | 0.720348 | -0.701621 | -0.974568 | 3.848926e-01 | -0.740085 | -0.751536 | -0.884185 | -0.873371 | 0.476731 |
| Coquimbo | 0.994747 | -0.618348 | -0.559017 | -0.603261 | -0.355718 | -0.356606 | -0.366735 | -0.354344 | -0.351405 | -0.396208 | -0.261024 | -1.064369 | -0.503423 | -0.267261 | -0.454859 | 2.719600 | -0.537409 | 2.677726 | -0.822226 | -0.508888 | -0.437311 | -0.263819 | -0.052129 | -0.141667 | -1.269757 | -0.709891 | -0.310096 | -1.236250 | -0.576151 | -0.410550 | 1.288645 | 0.223127 | -0.662828 | -0.663528 | -0.460599 | -1.396089 | -0.975820 | 2.193046 | -0.755610 | 1.736318 | -0.627570 | 0.589280 | -0.380235 | -0.700582 | -4.306269e-01 | 1.240347 | 0.476731 | 0.517268 | -0.351901 | -0.131972 | -0.277084 | 0.048967 | -0.400178 | 0.415959 | -0.234155 | -0.267490 | -0.238749 | -0.524389 | -0.328122 | -0.372854 | -0.200109 | 0.348769 | -0.695918 | -0.017723 | 2.517531 | -0.489045 | -0.248206 | 0.816999 | 3.405012 | -0.122213 | -0.367996 | -6.560749e-01 | -0.553020 | -0.243053 | 0.767224 | -0.287633 | -0.459525 | -0.614762 | -0.640329 | 0.157934 | -0.792406 | 0.185461 | -0.371418 | -0.700765 | -1.674999 | -0.275873 | -0.101168 | -0.227780 | -0.186772 | -1.052428 | -0.524389 | -0.516185 | -0.490027 | -0.753819 | -0.296847 | -0.200071 | -0.627036 | -0.710235 | 1.824327 | -0.549390 | -0.374165 | -0.298713 | -0.076796 | -0.353494 | -0.424798 | -0.359829 | -3.776744e-01 | -0.293679 | -0.203208 | 0.286115 | -0.422208 | -0.351766 | 1.749547 | -0.565187 | -0.399034 | -0.264153 | -0.250575 | -0.585376 | 0.588459 | 0.698645 | 0.244084 | -3.681311e-01 | -0.183891 | 0.945462 | 0.337347 | -0.135932 | 1.906925 |
| Valparaíso | 0.231731 | 1.427656 | 2.795085 | 1.537341 | 0.833647 | 0.251245 | 1.204986 | -0.173145 | -0.199258 | 0.720180 | 0.554676 | -0.909062 | -0.098887 | 0.534522 | 0.682288 | 1.262672 | -0.335881 | 1.343100 | 1.130561 | 0.696373 | 3.640321 | 2.331823 | 0.299739 | 3.369953 | -0.916559 | -0.565058 | 0.679811 | -0.171909 | 2.016529 | -0.410550 | 2.749996 | 3.392878 | 0.597479 | 1.327055 | -0.760990 | -1.396089 | -0.545311 | 1.035911 | -0.319681 | 2.435507 | -0.373149 | -0.580615 | -0.855528 | 1.495273 | -4.306269e-01 | 2.170608 | 1.906925 | 0.013683 | -0.332855 | -0.433572 | -0.145139 | 0.518234 | 0.856194 | -0.731513 | 1.587050 | 0.688186 | 0.506535 | -0.368515 | 0.380711 | -0.129935 | -0.410158 | 2.026315 | -0.120893 | -0.580075 | -0.204124 | 0.921662 | 0.319723 | 2.685464 | 0.849439 | 1.532153 | 0.074523 | 2.118631e-01 | -0.333213 | -1.671223 | 0.751637 | 0.365125 | 0.180040 | 2.332293 | 0.782855 | -0.456045 | -0.792406 | 1.004495 | 1.295121 | -0.025716 | 0.553550 | 0.452829 | 0.910515 | -0.626589 | 1.494175 | 1.151982 | -0.368515 | -0.516185 | -0.037183 | 0.536928 | 0.583817 | -0.200071 | -0.337053 | 0.362465 | 0.077779 | -0.129568 | 0.248209 | 0.028493 | -0.176966 | 0.254231 | 0.737122 | 0.534818 | -3.418327e-01 | 0.465305 | 0.969144 | 0.094764 | -0.085988 | -0.065445 | -0.571626 | -0.152369 | -0.312921 | 0.452371 | 0.262237 | -0.684992 | -0.689542 | 0.027050 | -0.524494 | -4.269976e-01 | 0.524283 | 0.006527 | 0.719887 | 0.211166 | -0.953463 |
| Metropolitana | -0.616065 | 2.723459 | -0.559017 | -0.700561 | 3.485167 | 3.047358 | 0.525323 | 3.632022 | 3.721352 | 3.051461 | 3.491193 | -0.815878 | 3.541940 | -0.668153 | -0.454859 | 0.777029 | -0.335881 | -1.389706 | -0.102778 | 0.696373 | -0.082735 | 2.119066 | 3.271070 | 0.856016 | -0.891773 | -0.678406 | 3.570787 | 0.529198 | 2.664699 | -0.410550 | -1.169080 | -0.677230 | -0.802862 | -1.134029 | -0.986283 | -0.690201 | -0.330057 | -1.278358 | -0.029062 | 0.163144 | -0.627570 | -0.580615 | -0.855528 | -0.700582 | -4.306269e-01 | -0.620174 | -0.953463 | 0.415838 | 1.046635 | 0.809134 | -0.013194 | -0.093853 | 1.693775 | -1.305249 | 2.757824 | 3.460544 | 3.555423 | -0.587648 | 3.548131 | 2.841909 | -1.259731 | 1.953914 | -0.863790 | 2.497524 | 0.476290 | 1.062732 | 3.664195 | 2.107602 | 0.740581 | 1.668068 | 3.668526 | 2.347199e+00 | -0.119431 | -1.226533 | 2.428629 | 3.588997 | -0.270914 | -0.614762 | 3.186253 | 0.248718 | -0.792406 | -1.389249 | -0.999731 | 2.963788 | 0.050106 | 1.320349 | -0.607010 | -1.194483 | 3.175121 | 1.009762 | -0.587648 | -0.229416 | -0.428654 | 2.153925 | 3.283423 | 3.658450 | 0.432033 | 0.421243 | -0.494572 | -0.407151 | 0.905159 | 3.061756 | -0.702855 | 3.582885 | 3.402018 | 3.522474 | 3.697959e+00 | 3.459654 | 1.125458 | -0.670638 | -0.181200 | 3.697638 | -0.368682 | 3.391107 | 2.907840 | 3.665005 | 0.737921 | -0.953872 | 2.549305 | 3.129454 | -1.446739 | -9.602287e-17 | 2.996435 | 2.025096 | -1.358777 | 2.197183 | -0.953463 |
| O'Higgins | 1.757764 | -0.209147 | 1.118034 | 0.077840 | -0.192044 | -0.478176 | 0.397886 | 0.189252 | -0.243896 | 0.063481 | -0.587304 | -1.043661 | -0.368577 | 2.138090 | 1.819435 | 0.777029 | 2.485518 | 0.135581 | -0.719448 | -0.508888 | -0.437311 | -0.306371 | -0.521286 | 0.602368 | -0.619129 | -0.558761 | 0.010499 | -1.097174 | 0.072019 | 0.844635 | -0.371980 | 0.821310 | -0.522794 | -0.446373 | -0.911185 | -0.690201 | -0.760565 | -0.947748 | 0.261557 | -0.536045 | -0.118729 | -0.450626 | -0.380235 | 0.397345 | -4.306269e-01 | -0.620174 | -0.953463 | 0.205331 | -0.433528 | -0.469607 | 1.570145 | -1.216013 | -0.679371 | -1.018381 | -0.234155 | -0.428269 | -0.464593 | -0.434389 | 0.153841 | -0.094421 | -0.914651 | -0.468450 | -1.050453 | -2.042190 | 0.476290 | -0.771186 | -0.311309 | -0.486152 | -0.482777 | -1.097303 | -0.470219 | 8.031311e-17 | -0.553020 | -0.709394 | -0.191618 | -0.131441 | -0.791516 | -0.614762 | -0.691941 | -0.600698 | -1.452744 | -0.849583 | 1.475749 | -0.990072 | -1.195517 | -0.664094 | -0.607010 | -0.447591 | -0.186772 | 0.796432 | -0.434389 | -0.802955 | -0.405811 | -1.278511 | 0.048834 | -0.200071 | -1.698713 | -0.680846 | -0.855684 | -0.549390 | -0.374165 | -0.469648 | 0.624389 | -0.203408 | -0.371176 | -0.247409 | -1.817148e-02 | -0.269268 | -0.203208 | -0.369944 | -0.422863 | -0.188154 | -0.635046 | 0.124200 | -0.446370 | -0.269654 | -0.044503 | -0.689987 | -0.913848 | -0.644146 | 0.105427 | -8.096924e-01 | -0.466447 | -0.661217 | -0.298431 | -0.224305 | -0.953463 |
| Maule | 1.503425 | 0.063653 | -0.559017 | -0.700561 | -0.333895 | -0.356606 | -1.895977 | -0.052346 | 0.011359 | -0.166364 | -0.424164 | -1.074722 | -0.368577 | 2.138090 | -0.454859 | -0.679900 | 1.276347 | 0.008474 | -0.616670 | 0.294619 | -0.437311 | 0.076593 | -0.013032 | -0.581324 | -0.532379 | -0.697297 | -0.276349 | -0.630042 | 0.288076 | -0.410550 | 0.026570 | -0.196217 | -1.222965 | -0.518758 | 0.740964 | 1.427462 | 0.315706 | -0.451833 | 0.116248 | 0.862333 | 0.135691 | -0.580615 | -0.855528 | 0.711039 | -4.306269e-01 | -0.620174 | -0.953463 | -0.898684 | -0.536922 | -1.189355 | 0.184723 | 0.436622 | -1.377355 | -0.946664 | -0.624413 | -0.424545 | -0.393613 | -0.472778 | 0.171651 | -0.516332 | -1.093755 | -0.655528 | -0.904505 | -0.712994 | -0.204124 | -0.630116 | -0.090448 | -0.521005 | -0.482777 | -0.406235 | -0.460022 | -6.528849e-01 | -0.553020 | -0.602802 | 0.215118 | -0.061316 | -0.407268 | -0.614762 | -0.689057 | 0.177622 | -0.132068 | -1.033721 | -0.769073 | -0.604330 | -0.320759 | -1.340920 | 0.910515 | -0.873512 | -0.560316 | -0.270218 | -0.472778 | 0.057354 | 0.617034 | -1.399753 | 0.180522 | -0.521615 | -1.169178 | -0.577984 | -1.463242 | -0.312109 | 0.161768 | 0.179011 | 1.951632 | -0.152948 | -0.283952 | 0.173260 | -3.266272e-01 | 0.064793 | 0.109420 | 1.242867 | -0.427314 | -0.269960 | -0.571626 | -0.566815 | -0.579103 | -0.325629 | -0.479322 | -0.739933 | -0.326965 | -0.675162 | -0.023689 | -7.964951e-01 | 0.446097 | -0.510508 | -0.181958 | 0.068356 | 0.476731 |
| Biobío | -0.531285 | 1.086656 | -0.559017 | -0.797861 | -0.279337 | 1.223806 | -0.239298 | 0.128852 | -0.094893 | 0.950024 | 0.554676 | -0.836585 | 0.845031 | -0.668153 | -0.454859 | -0.679900 | 0.067176 | -0.881277 | 1.336118 | -0.107134 | -0.437311 | -1.072298 | 1.003476 | 0.072525 | -0.036662 | -0.558761 | 0.032997 | -0.726577 | -0.360095 | 0.638706 | 0.491545 | -0.251719 | -0.802862 | -0.989259 | 0.440573 | -0.690201 | -0.760565 | -0.617138 | -0.319681 | -1.060436 | -0.627570 | -0.450626 | 2.946818 | -0.700582 | -4.306269e-01 | -0.620174 | -0.953463 | -0.815943 | -0.267554 | -0.669181 | -0.277084 | 0.028564 | -0.120984 | -1.018381 | 1.066706 | 0.711154 | 0.519440 | -0.683383 | 0.017116 | -0.296143 | -1.203468 | 0.053838 | -0.953363 | 0.064074 | -0.544331 | -0.347974 | -0.153551 | -0.433872 | -0.436124 | 1.259932 | -0.337412 | -6.594967e-01 | -0.553020 | -0.985035 | 1.524147 | 0.350357 | -0.512695 | -0.614762 | -0.689141 | 2.043257 | -0.792406 | -1.117365 | -1.221327 | -0.025716 | 0.420075 | -1.134581 | -0.269782 | -0.724542 | 0.373544 | -0.127998 | -0.683383 | -0.516185 | 0.141347 | -0.549132 | 0.847193 | -0.414434 | 0.280737 | 0.641661 | -0.530676 | -0.549390 | 0.040751 | 1.456407 | -1.053447 | 0.459377 | 0.740691 | 0.344066 | -4.118869e-01 | 0.660589 | 1.281772 | 1.242867 | -0.411127 | 0.016361 | -0.432102 | -0.182849 | -0.186823 | -0.247127 | 0.309082 | -0.747425 | -0.284657 | 0.169799 | 1.860898 | -5.589440e-01 | 1.219232 | 0.423448 | 3.033208 | 2.060316 | 1.906925 |
| Araucanía | 0.570849 | -0.209147 | -0.559017 | -0.603261 | 0.124392 | -0.599746 | -1.449948 | -0.112746 | -0.289163 | 0.785850 | 0.065256 | 1.876105 | -0.503423 | 1.336306 | 2.956582 | -0.679900 | 1.880932 | -0.690616 | -0.411113 | 3.106895 | -0.437311 | -0.987195 | 0.104257 | -0.575688 | 0.502429 | -0.130560 | -0.265100 | -0.308531 | 0.504132 | -0.410550 | 0.491545 | -0.603228 | 1.857786 | 1.616595 | 2.843699 | 0.956870 | 2.468250 | -0.782443 | 0.261557 | 0.337941 | -0.627570 | -0.450626 | 0.570352 | 2.906894 | -4.306269e-01 | -0.620174 | 0.476731 | -0.199033 | 0.023581 | -1.112122 | 0.910420 | 0.212190 | -1.237758 | -0.588079 | -0.104069 | -0.317462 | -0.296823 | -0.461338 | -0.428131 | -0.982281 | -0.737422 | 0.498065 | -0.439725 | 0.207218 | 1.496910 | 1.062732 | -0.311309 | -0.437358 | -0.353184 | -0.162528 | -0.298595 | -3.030555e-01 | -0.553020 | -0.490380 | 0.468371 | -0.259311 | 0.775251 | -0.614762 | -0.312432 | 1.257007 | 0.528271 | -1.014376 | -1.093167 | 0.842204 | 0.134984 | -0.897944 | 3.271109 | -0.523971 | -0.373544 | -0.839098 | -0.461338 | 2.351511 | 0.357523 | -0.980929 | 0.081756 | 0.014291 | 0.545505 | 0.318381 | -0.164824 | -0.301548 | -0.840946 | 0.435268 | 0.424050 | -0.097212 | -0.076991 | -0.118670 | -2.565730e-01 | -0.082122 | 1.359928 | 1.871590 | -0.343890 | -0.310863 | 1.914439 | -0.445087 | -0.062981 | -0.197030 | -0.713302 | -0.726337 | 0.217846 | -0.327141 | -0.787585 | -6.677754e-01 | -0.139835 | 0.718426 | 0.000000 | 0.357106 | 0.476731 |
| Los Ríos | -0.700845 | -0.481948 | -0.559017 | -0.700561 | -0.563039 | -0.235036 | -0.812764 | -0.414743 | -0.342603 | -0.724558 | -0.261024 | 0.488698 | -0.233732 | -0.267261 | -0.454859 | -0.194257 | -0.738938 | -1.071938 | -0.513892 | -0.107134 | -0.437311 | -1.072298 | -0.364900 | -0.378406 | 1.537238 | -0.300581 | -0.332594 | -0.421428 | -0.792208 | -0.410550 | -0.504830 | -0.615562 | -0.942896 | -0.120641 | -0.010013 | -0.690201 | 1.822487 | -0.617138 | -0.755610 | -1.235233 | -0.627570 | 0.069327 | -0.380235 | -0.700582 | 1.657385e-15 | -0.620174 | 0.476731 | -1.059410 | -0.490667 | -0.914058 | 0.316668 | -1.256818 | -0.958565 | -0.444645 | -0.624413 | -0.399714 | -0.325860 | -0.472837 | -0.384566 | -0.119991 | 0.107463 | -0.608259 | -0.707819 | 0.258341 | -1.224745 | 0.216308 | -0.500619 | -0.332450 | -0.482777 | -0.799543 | -0.421096 | -6.330079e-01 | -0.553020 | -0.153948 | -0.201151 | -0.497619 | -0.868449 | 0.050702 | -0.691941 | -0.689341 | -0.792406 | 0.012520 | -0.292070 | -0.700765 | 1.161805 | -0.734043 | -0.269782 | -0.376458 | -0.560316 | 0.867542 | -0.472837 | 2.064742 | -1.871499 | -0.092990 | -0.527301 | -0.414434 | -0.803547 | 0.053880 | 0.419596 | -0.549390 | -2.068406 | -0.666027 | -0.302177 | -0.528634 | -0.499916 | -0.543509 | -4.162314e-01 | -0.433361 | 0.109420 | 0.832830 | -0.425497 | -0.310863 | -0.495522 | -0.436538 | -0.569938 | -0.378269 | -0.788305 | -0.266827 | -0.344456 | -0.433789 | 1.050481 | -6.183318e-01 | -0.482279 | -0.220693 | -0.180918 | -0.762745 | -0.953463 |
| Los Lagos | -0.870404 | 0.541054 | 1.118034 | -0.797861 | -0.224779 | 0.129675 | -0.048143 | -0.112746 | -0.230693 | -0.100694 | -0.424164 | 0.913203 | -0.233732 | -0.668153 | -0.454859 | -0.679900 | -0.738938 | 0.707564 | -0.719448 | 0.294619 | -0.437311 | 0.927623 | 0.065161 | -0.158577 | 2.169276 | 0.266157 | -0.405712 | -0.473786 | -0.576151 | -0.410550 | 0.026570 | -0.701897 | 0.457445 | 0.241283 | 0.290378 | 0.956870 | 0.746215 | 0.374691 | -0.319681 | -0.361247 | -0.627570 | 3.449024 | -0.855528 | -0.700582 | 3.301473e+00 | 2.170608 | 0.476731 | 0.187151 | 3.470945 | -0.657185 | -0.145139 | -0.869163 | -1.237758 | -0.444645 | 0.156103 | -0.271835 | -0.280691 | -0.109448 | 0.109727 | -0.574576 | 0.581948 | 1.280998 | 0.044474 | -0.385808 | -0.884538 | 2.755581 | -0.122000 | -0.332450 | -0.233959 | 0.387483 | 0.064338 | 5.209139e-02 | 3.146505 | -0.258875 | -0.185306 | -0.243263 | 2.629382 | 2.047094 | -0.039476 | 2.124835 | 0.528271 | 0.118003 | -0.204269 | 0.745769 | -0.591294 | -0.316062 | -0.269782 | 0.025849 | -0.373544 | -0.056888 | -0.109448 | 0.917663 | 0.650857 | -0.553296 | -0.181620 | 0.121472 | -0.185757 | -0.960042 | 0.512610 | -0.549390 | -0.754505 | -0.325844 | -0.652770 | -0.295081 | -0.073576 | -0.325015 | -3.277133e-01 | -0.145860 | -2.157128 | 0.149436 | -0.317904 | -0.392670 | -0.914094 | -0.545401 | 2.067430 | -0.349799 | -0.245093 | 0.116098 | -0.911957 | 1.206767 | 1.193196 | -2.097769e-01 | 0.512154 | 1.739115 | 0.515001 | 0.764723 | 0.476731 |
| Aysén | -0.955183 | -0.822948 | -0.559017 | -0.603261 | -0.737624 | -1.207597 | -1.110116 | -0.595941 | -0.370895 | -0.724558 | -0.587304 | 1.016741 | -0.503423 | -0.668153 | -0.454859 | -0.679900 | -0.738938 | -0.182187 | -0.822226 | -0.910642 | -0.348667 | -0.646783 | -0.716768 | -0.688420 | 1.431898 | 1.745972 | -0.495703 | 0.272316 | -0.792208 | -0.410550 | -1.169080 | -0.695731 | 1.717752 | 2.123289 | 0.891159 | 0.486278 | 0.100452 | -0.451833 | 2.586510 | -0.885639 | -0.627570 | 0.459292 | 0.570352 | 0.240498 | 1.435423e+00 | -0.620174 | 1.906925 | 1.284880 | -0.316530 | 0.770348 | 1.438200 | -0.501911 | -0.679371 | 2.065449 | -0.754499 | -0.517660 | -0.493630 | 1.282215 | -0.599654 | -1.604493 | 1.860058 | -1.009879 | -0.003132 | -1.398041 | -1.224745 | -1.053328 | -0.500619 | -0.521005 | -0.384286 | -1.428005 | -0.450049 | -4.129872e-01 | -0.553020 | 1.569849 | -1.034075 | -0.603383 | -0.640792 | -0.614762 | -0.501923 | -0.861020 | 1.188609 | 2.340413 | 0.833425 | -0.122152 | 0.505849 | -0.637751 | -0.607010 | 1.991907 | -0.747087 | 2.076412 | 1.282215 | -0.229416 | 0.507328 | -0.421483 | -0.724833 | -0.521615 | 0.167266 | -0.151843 | 2.312351 | 1.806187 | 0.000000 | -0.654476 | -0.176966 | -0.557729 | -0.491070 | -0.615313 | -6.173829e-17 | -0.570783 | -1.453717 | -1.654726 | -0.239801 | -0.433573 | -1.040934 | -0.551019 | -0.417625 | -0.410414 | -0.711558 | 1.987437 | -0.686942 | -0.805315 | -1.344744 | 2.149798e+00 | -0.805176 | -1.095859 | 0.672315 | -0.911947 | 0.476731 |
| Magallanes y Antártica | -0.955183 | -0.891148 | -0.559017 | -0.603261 | -0.432100 | -0.478176 | 1.629775 | -0.475143 | -0.399186 | -0.658888 | -0.587304 | 1.109925 | -0.098887 | -0.668153 | -0.454859 | -0.679900 | -0.335881 | -0.690616 | 0.616670 | -0.910642 | -0.260023 | -0.476577 | -0.677672 | -0.496775 | -0.067645 | 2.873150 | -0.484454 | 0.001527 | -0.144038 | -0.410550 | -1.169080 | -0.695731 | 1.437684 | 1.001324 | 0.740964 | 0.956870 | -1.191074 | 1.531826 | 2.150581 | 0.337941 | -0.627570 | -0.580615 | -0.380235 | -0.543736 | -4.306269e-01 | 0.310087 | 0.476731 | -0.268692 | -0.585898 | 2.508457 | 0.316668 | 1.354752 | 0.297807 | 1.635147 | -0.364241 | -0.419578 | -0.335539 | 3.346638 | -0.561569 | -0.736521 | 2.228582 | -0.642380 | 2.796824 | 0.176544 | -0.204124 | -0.347974 | -0.437516 | -0.521005 | -0.420572 | -1.019545 | 0.086400 | 5.976015e-02 | -0.497172 | 1.189281 | -1.019132 | -0.515456 | 1.168712 | 1.096431 | 0.621928 | -0.883807 | 1.848947 | 1.370333 | 2.231003 | -0.411459 | 2.103300 | 1.869415 | -0.607010 | 2.671164 | -0.560316 | -0.910208 | 3.346638 | -1.376494 | -0.070909 | 1.603608 | -0.716603 | -0.200071 | -0.690076 | -1.268627 | -0.809562 | 0.879473 | -0.875522 | -0.311249 | -0.953278 | -0.577694 | -0.554858 | -0.572158 | -6.173829e-17 | -0.685150 | -0.672149 | -1.408704 | 0.220001 | -0.433573 | 0.861667 | -0.587307 | -0.700011 | -0.237696 | -0.707072 | 2.633969 | -1.028010 | -0.701036 | 1.212137 | 2.615002e+00 | -0.835946 | -0.641776 | -0.538923 | -0.870161 | -0.953463 |
In [97]:
# Calculate eigenvalues and vectors
cov_mat = np.cov(chile_data_s.T)
eig_val, eig_vec = np.linalg.eig(cov_mat)
# Print
print('Eigenvectors \n%s' %eig_vec)
print('\nEigenvalues \n%s' %eig_val)
Eigenvectors [[-0.01022173+0.j 0.14285067+0.j -0.05784359+0.j ... -0.01858377+0.j -0.00991488+0.j -0.02262521+0.j] [ 0.14103736+0.j 0.02401269+0.j 0.04968103+0.j ... -0.00698431+0.j 0.00050155+0.j -0.01439759+0.j] [ 0.0142111 +0.j 0.03064968+0.j -0.00099107+0.j ... -0.00256709+0.j -0.03854842+0.j 0.00493263+0.j] ... [-0.01871132+0.j 0.09444283+0.j 0.07914181+0.j ... 0.09367178+0.j 0.12321427+0.j -0.02054219+0.j] [ 0.12292742+0.j 0.05445328+0.j 0.0668449 +0.j ... -0.03881707+0.j 0.05321002+0.j 0.00253113+0.j] [-0.02281744+0.j 0.10240462+0.j 0.00371734+0.j ... -0.07188785+0.j 0.16661665+0.j -0.01150759+0.j]] Eigenvalues [ 4.50766815e+01+0.00000000e+00j 1.83868353e+01+0.00000000e+00j 1.23853547e+01+0.00000000e+00j 1.16143194e+01+0.00000000e+00j 9.29727743e+00+0.00000000e+00j 6.89218885e+00+0.00000000e+00j 6.39938852e+00+0.00000000e+00j 4.97922846e+00+0.00000000e+00j 4.52705760e+00+0.00000000e+00j 5.52499045e+00+0.00000000e+00j 3.71034539e+00+0.00000000e+00j 2.52982074e+00+0.00000000e+00j 1.64930914e+00+0.00000000e+00j 3.09863111e+00+0.00000000e+00j 2.47580958e-15+0.00000000e+00j -1.65288848e-15+0.00000000e+00j 5.70081743e-16+1.20046461e-15j 5.70081743e-16-1.20046461e-15j 1.33129042e-15+4.80421842e-16j 1.33129042e-15-4.80421842e-16j 1.44519840e-15+0.00000000e+00j 1.40636211e-15+0.00000000e+00j -1.47858887e-15+8.84405337e-17j -1.47858887e-15-8.84405337e-17j -1.47610964e-15+0.00000000e+00j -1.33943776e-15+3.03287179e-16j -1.33943776e-15-3.03287179e-16j 1.17479495e-15+2.21117339e-17j 1.17479495e-15-2.21117339e-17j -1.15151539e-15+8.26742487e-17j -1.15151539e-15-8.26742487e-17j -1.12802567e-15+0.00000000e+00j -1.08451833e-15+0.00000000e+00j 1.02197629e-15+2.85596995e-16j 1.02197629e-15-2.85596995e-16j 1.02268194e-15+0.00000000e+00j 9.89138793e-16+6.13869191e-17j 9.89138793e-16-6.13869191e-17j -9.80675487e-16+3.95311889e-17j -9.80675487e-16-3.95311889e-17j -6.98934425e-16+4.62379327e-16j -6.98934425e-16-4.62379327e-16j 6.52409059e-17+7.09044637e-16j 6.52409059e-17-7.09044637e-16j -7.44399213e-16+3.34330406e-16j -7.44399213e-16-3.34330406e-16j 6.04362753e-16+4.45088804e-16j 6.04362753e-16-4.45088804e-16j 8.39313147e-16+0.00000000e+00j 7.77349757e-16+1.90177405e-17j 7.77349757e-16-1.90177405e-17j -7.81922370e-16+4.84393310e-17j -7.81922370e-16-4.84393310e-17j -3.65466314e-16+5.25458894e-16j -3.65466314e-16-5.25458894e-16j 6.67557910e-16+2.40530111e-16j 6.67557910e-16-2.40530111e-16j -6.90801669e-16+1.90538043e-16j -6.90801669e-16-1.90538043e-16j 6.89579968e-16+0.00000000e+00j -6.90425746e-16+8.22982436e-17j -6.90425746e-16-8.22982436e-17j 5.84160501e-16+1.62731053e-16j 5.84160501e-16-1.62731053e-16j 5.92981668e-16+6.50535753e-17j 5.92981668e-16-6.50535753e-17j -5.67859413e-16+2.10055555e-16j -5.67859413e-16-2.10055555e-16j -6.37057598e-16+0.00000000e+00j 4.39735544e-16+2.62183104e-16j 4.39735544e-16-2.62183104e-16j -5.57996374e-16+0.00000000e+00j 4.38732849e-16+2.32957100e-16j 4.38732849e-16-2.32957100e-16j -4.98811569e-16+1.88570905e-16j -4.98811569e-16-1.88570905e-16j -7.62149586e-17+3.94355055e-16j -7.62149586e-17-3.94355055e-16j 9.88091240e-17+3.76443403e-16j 9.88091240e-17-3.76443403e-16j 2.19555183e-16+3.26239636e-16j 2.19555183e-16-3.26239636e-16j -4.04160113e-16+2.28606742e-16j -4.04160113e-16-2.28606742e-16j 4.43182710e-16+0.00000000e+00j -5.03655195e-16+4.06385692e-17j -5.03655195e-16-4.06385692e-17j 4.26953284e-16+1.00903760e-16j 4.26953284e-16-1.00903760e-16j -1.33926664e-16+2.59310667e-16j -1.33926664e-16-2.59310667e-16j 2.31675633e-16+2.27615670e-16j 2.31675633e-16-2.27615670e-16j -3.64022954e-16+1.17142945e-16j -3.64022954e-16-1.17142945e-16j 3.61809868e-16+1.00643295e-16j 3.61809868e-16-1.00643295e-16j -2.97742905e-16+1.58398709e-16j -2.97742905e-16-1.58398709e-16j -3.69130280e-16+5.85995989e-17j -3.69130280e-16-5.85995989e-17j 1.16237986e-16+2.25550972e-16j 1.16237986e-16-2.25550972e-16j -1.26423005e-17+2.02639856e-16j -1.26423005e-17-2.02639856e-16j 3.12944063e-16+0.00000000e+00j -2.55543703e-16+1.35768914e-16j -2.55543703e-16-1.35768914e-16j -3.07479316e-16+0.00000000e+00j 2.33658396e-16+1.03418260e-16j 2.33658396e-16-1.03418260e-16j 3.00322137e-16+0.00000000e+00j -1.70117825e-17+1.55617544e-16j -1.70117825e-17-1.55617544e-16j 1.18175179e-16+9.81707997e-17j 1.18175179e-16-9.81707997e-17j 1.93400054e-16+0.00000000e+00j 2.52266634e-16+0.00000000e+00j -2.82466192e-17+5.94471069e-17j -2.82466192e-17-5.94471069e-17j 7.82410154e-17+0.00000000e+00j 1.37243778e-16+0.00000000e+00j -1.56616772e-16+4.90419987e-17j -1.56616772e-16-4.90419987e-17j -2.22041433e-16+0.00000000e+00j -8.73831456e-17+0.00000000e+00j -2.47697797e-32+0.00000000e+00j]
In [98]:
# Run PCA and fit the model
myPCA = PCA()
x = myPCA.fit(chile_data_s)
In [99]:
# Plotting the varaince explained by each component
plt.bar(range(1,len(x.explained_variance_ )+1),x.explained_variance_ratio_)
plt.ylabel('Explained variance')
plt.xlabel('Components')
plt.title('All Principle Components')
Out[99]:
Text(0.5, 1.0, 'All Principle Components')
In [100]:
# Deciding on the number of principal componenets to chose
plt.plot(range(1, len(x.explained_variance_)+1), x.explained_variance_ratio_.cumsum())
plt.ylabel('Explained variance')
plt.xlabel('Components')
pass
In [101]:
# Calculate the numeric values of principal components
x.explained_variance_ratio_.cumsum()
Out[101]:
array([0.3312722 , 0.46639855, 0.55741953, 0.64277411, 0.71110055,
0.76175181, 0.80878144, 0.84938504, 0.8859778 , 0.91924751,
0.94651514, 0.96928723, 0.98787909, 1. , 1. ])
will use only first 7 components, which explain 80.9 % of variance. instead of 127 columns in our dataset we have 7 uncorrelated components, which make the analysis easier.
In [102]:
# Calculate loadings
myPCA = PCA(n_components = 7)
pca_model = myPCA.fit(chile_data_s)
# Print
print("The loadings are are \n {}".format(pca_model.components_))
The loadings are are [[-1.02217277e-02 1.41037360e-01 1.42111012e-02 -1.37097650e-02 1.48126933e-01 1.41714050e-01 2.62006734e-02 1.45785498e-01 1.42282465e-01 1.43981514e-01 1.49518705e-01 -6.39023470e-02 1.42446079e-01 -6.39178387e-03 5.96387885e-04 5.32322647e-02 -1.63127131e-05 -3.30567184e-02 2.43490034e-02 5.21125334e-02 3.18376106e-02 1.05020380e-01 1.48514167e-01 7.63980384e-02 -5.32750221e-02 -7.06357362e-02 1.47735023e-01 2.71380925e-04 1.28537731e-01 -1.85715489e-02 1.13235572e-02 1.62682207e-02 -5.14656908e-02 -5.55592351e-02 -4.38927341e-02 -4.87076457e-02 -1.45880074e-02 -4.25539300e-02 -3.84577670e-02 3.47270343e-02 -2.96110996e-02 -2.15771150e-02 -1.96119212e-02 -8.84224563e-03 -2.71928802e-02 2.39140242e-03 -4.22270005e-02 6.26971115e-03 4.86259746e-02 -2.99982637e-03 -1.15861076e-02 -1.11659579e-02 6.43580249e-02 -9.89538209e-02 1.40895726e-01 1.49377598e-01 1.48704776e-01 -6.45389930e-02 1.47072963e-01 1.15175790e-01 -9.28076189e-02 1.16292747e-01 -6.34460110e-02 9.57160535e-02 2.77316430e-02 6.52916011e-02 1.47341008e-01 1.11729312e-01 4.44651515e-02 1.12103095e-01 1.39411096e-01 8.15840454e-02 -1.31390988e-03 -9.63317592e-02 1.31666069e-01 1.50513770e-01 -1.00911201e-03 -5.60410948e-03 1.21231940e-01 4.47844192e-02 -6.19518778e-02 -8.31992133e-02 -5.39516607e-02 1.18082096e-01 -9.26207288e-03 3.72414462e-02 2.71188256e-03 -9.30911277e-02 1.47610093e-01 3.94378418e-02 -6.45389930e-02 -5.39361158e-03 -1.51012060e-02 6.85642046e-02 1.49296323e-01 1.37780814e-01 1.90113337e-02 3.23552136e-02 -3.02192455e-02 -5.01482406e-02 3.23122380e-02 1.38453321e-01 -2.75134702e-02 1.50327402e-01 1.49832735e-01 1.50551386e-01 1.31343718e-01 1.50786519e-01 7.38468273e-02 1.30646124e-02 -1.47740135e-02 1.44507354e-01 -2.05268518e-02 1.31076361e-01 1.17723336e-01 1.44948459e-01 4.89221440e-02 -8.58789890e-02 9.97237194e-02 1.35835987e-01 -3.85195938e-02 -4.62745837e-02 1.43469832e-01 1.05095446e-01 -1.87113188e-02 1.22927421e-01 -2.28174352e-02] [-1.42850674e-01 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In [103]:
# Explore the importance of each feature for principle components
pca = PCA(n_components = 7).fit(chile_data_s)
vars = pca.explained_variance_ratio_
c_names = chile_data_s.columns
sum = 0
print('Variance: Projected dimension')
print('------------------------------')
for idx, row in enumerate(pca.components_):
output = '{0:4.1f}%: '.format(100.0 * vars[idx])
output += " + ".join("{0:5.2f} * {1:s}".format(val, name) \
for val, name in zip(row, c_names))
sum += 100*vars[idx]
print(output)
print('Total variance explained by the 7 components {0:4.1f}%'.format(sum))
# Total variance explained by the 7 components 80.9%
Variance: Projected dimension ------------------------------ 33.1%: -0.01 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + 0.14 * NUMBER OF CULTURAL CENTERS + 0.01 * WORLD CULTURAL HERITAGE SITES + -0.01 * NUMBER OF ARCHEOLOGICAL SITES + 0.15 * NATIONAL MONUMENTS + 0.14 * MUSEUMS + 0.03 * % OF POPULATION THAT ATTENDS MUSEUMS + 0.15 * THEATERS + 0.14 * NUMBER OF THEATER PLAYS PER YEAR + 0.14 * LIBRARIES + 0.15 * GALERIES + -0.06 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + 0.14 * NUMBER OF EXHIBITS + -0.01 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + 0.00 * MAJOR SPORTS EVENTS PER YEAR + 0.05 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.00 * ARTWORK SITES + -0.03 * POPULAR ARCHITECTURE SITES + 0.02 * HISTORICAL SITES + 0.05 * LOCAL MARKETS + 0.03 * CULTURAL SITES LEVEL III (INTERNATIONAL) + 0.11 * CULTURA SITES LEVEL II (NATIONAL) + 0.15 * CULTURAL SITES LEVEL I (LOCAL) + 0.08 * HERITAGE ARCHITECTURAL HOUSES + -0.05 * % OF LAND THAT CORRESPONDS TO FORESTS + -0.07 * NATIONAL PROTECTED SITES (%) + 0.15 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + 0.00 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + 0.13 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.02 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + 0.01 * NUMBER OF BEACHES AND BEACH RESORTS + 0.02 * LAND AFFECTED BY WILDFIRES + -0.05 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.06 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.04 * RIVERS, LAKES AND WATERFALLS + -0.05 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.01 * GEISERS AND THERMAL CENTERS + -0.04 * PIERS AND SEASHORES + -0.04 * GLACIERS AND WINTER VACATION LOCATIONS + 0.03 * VALLEYS + -0.03 * DESERTS AND DUNES + -0.02 * ISLANDS AND PENINSULAS + -0.02 * PALEONTOLOGY SITES + -0.01 * HIKING TRAILS + -0.03 * PRESERVED SITES + 0.00 * SEASHORE PROTECTED SITES + -0.04 * BIOSHPERE RESERVES + 0.01 * % AVAILABLE WORKFORCE + 0.05 * % POPULATION ORIENTED TOWARDS TOURISM + -0.00 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.01 * 5 POPULATION WITH PRIMARY EDUCATION + -0.01 * % POPULATION WITH SECONDARY EDUCATION + 0.06 * AVERAGE NUMBER OF YEARS STUDYING + -0.10 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + 0.14 * TOURISM-ORIENTED INSTITUTIONS + 0.15 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + 0.15 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + -0.06 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + 0.15 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + 0.12 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + -0.09 * ROOMS PER 1000 HABITANTS + 0.12 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + -0.06 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + 0.10 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + 0.03 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + 0.07 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + 0.15 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + 0.11 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + 0.04 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + 0.11 * NATIONAL TOURISTS ARRIVALS + 0.14 * INTERNATIONAL TOURISTS ARRIVALS + 0.08 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + -0.00 * DENSITY OF AIRPORTS + -0.10 * DENSITY OF ROADS AND HIGHWAYS + 0.13 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + 0.15 * NUMBER OF VEHICLES + -0.00 * VISITORS TO PROTECTED SITES + -0.01 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + 0.12 * TOURIST'S ARRIVALS THROUGH BORDER LINES + 0.04 * SECONDARY ROADS (KMS) + -0.06 * NUMBER OF INTERNATIONAL BORDER GATES + -0.08 * Density of restaurants and other food services per 100,000 inhabitants + -0.05 * Density of People employed in restaurants and the like per 10,000 inhabitants + 0.12 * Car rental companies + -0.01 * Densidad de camas en hospitales por cada 10.000 habitantes + 0.04 * Density of beds in hospitals per 10,000 inhabitants + 0.00 * Number of spas + -0.09 * Density of gambling casinos per million inhabitants + 0.15 * Number of golf courses + 0.04 * Number of craft centers + -0.06 * Density of tour guides per 100,000 inhabitants + -0.01 * Number of thermal centers + -0.02 * Density of Sports Facilities and Venues per 10,000 inhabitants + 0.07 * Penetration of telephone lines in service per 100 inhabitants + 0.15 * Density of service stations + 0.14 * Number of tour-operator companies certified with the tourism quality seal + 0.02 * Perception of exposure to crime (%) + 0.03 * Percentage of victimized households with at least one victim + -0.03 * Density of homicides per million inhabitants + -0.05 * Density of crimes against public health per million inhabitants + 0.03 * Black figure index + 0.14 * Budget for public safety (Thousands of $) + -0.03 * Percentage of households that reported at least one crime + 0.15 * Number of declared crimes + 0.15 * Number of crimes investigated + 0.15 * Number of accidents (roads, air and waterways) + 0.13 * Illegal commerce + 0.15 * Number of Carabineros + 0.07 * Unemployment rate + 0.01 * Poverty rate + -0.01 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + 0.14 * Number of strikes carried out + -0.02 * Average (days) duration of a strike + 0.13 * Person-day cost of a strike + 0.12 * Density of Bank Branches per million inhabitants + 0.14 * Floating population + 0.05 * Volume of exports + -0.09 * Density of Tourist Information Offices per million inhabitants + 0.10 * Number of visits to Tourist Information Offices + 0.14 * Average monthly global searches by tourist attraction on the internet + -0.04 * National tourism promotion budget (Thousands of USD) + -0.05 * International tourism promotion budget (Thousands of USD) + 0.14 * Investments in public infrastructure made by the Ministry of Public Works + 0.11 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.02 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + 0.12 * Funds obtained from FNRD (Thousands of pesos) + -0.02 * Number of regional strategic development plans 13.5%: -0.14 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + -0.02 * NUMBER OF CULTURAL CENTERS + -0.03 * WORLD CULTURAL HERITAGE SITES + 0.05 * NUMBER OF ARCHEOLOGICAL SITES + 0.04 * NATIONAL MONUMENTS + 0.03 * MUSEUMS + 0.15 * % OF POPULATION THAT ATTENDS MUSEUMS + 0.03 * THEATERS + 0.05 * NUMBER OF THEATER PLAYS PER YEAR + -0.03 * LIBRARIES + 0.03 * GALERIES + 0.06 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + 0.06 * NUMBER OF EXHIBITS + -0.16 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + -0.12 * MAJOR SPORTS EVENTS PER YEAR + -0.03 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.14 * ARTWORK SITES + -0.05 * POPULAR ARCHITECTURE SITES + 0.06 * HISTORICAL SITES + -0.12 * LOCAL MARKETS + 0.01 * CULTURAL SITES LEVEL III (INTERNATIONAL) + 0.05 * CULTURA SITES LEVEL II (NATIONAL) + -0.00 * CULTURAL SITES LEVEL I (LOCAL) + -0.02 * HERITAGE ARCHITECTURAL HOUSES + 0.02 * % OF LAND THAT CORRESPONDS TO FORESTS + 0.16 * NATIONAL PROTECTED SITES (%) + 0.04 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + 0.11 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + 0.00 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.02 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + -0.12 * NUMBER OF BEACHES AND BEACH RESORTS + -0.05 * LAND AFFECTED BY WILDFIRES + 0.07 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + 0.04 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.06 * RIVERS, LAKES AND WATERFALLS + 0.04 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.10 * GEISERS AND THERMAL CENTERS + 0.06 * PIERS AND SEASHORES + 0.11 * GLACIERS AND WINTER VACATION LOCATIONS + -0.04 * VALLEYS + 0.03 * DESERTS AND DUNES + 0.00 * ISLANDS AND PENINSULAS + -0.03 * PALEONTOLOGY SITES + -0.11 * HIKING TRAILS + 0.03 * PRESERVED SITES + -0.00 * SEASHORE PROTECTED SITES + 0.04 * BIOSHPERE RESERVES + 0.06 * % AVAILABLE WORKFORCE + 0.01 * % POPULATION ORIENTED TOWARDS TOURISM + 0.22 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.03 * 5 POPULATION WITH PRIMARY EDUCATION + 0.05 * % POPULATION WITH SECONDARY EDUCATION + 0.14 * AVERAGE NUMBER OF YEARS STUDYING + 0.16 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + 0.01 * TOURISM-ORIENTED INSTITUTIONS + 0.04 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + 0.05 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + 0.18 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + 0.02 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + 0.06 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + 0.18 * ROOMS PER 1000 HABITANTS + -0.02 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + 0.18 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + 0.08 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + -0.08 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + -0.03 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + 0.05 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + 0.01 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + -0.03 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + -0.01 * NATIONAL TOURISTS ARRIVALS + 0.09 * INTERNATIONAL TOURISTS ARRIVALS + 0.08 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + 0.04 * DENSITY OF AIRPORTS + 0.14 * DENSITY OF ROADS AND HIGHWAYS + -0.09 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + 0.03 * NUMBER OF VEHICLES + 0.05 * VISITORS TO PROTECTED SITES + 0.05 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + 0.12 * TOURIST'S ARRIVALS THROUGH BORDER LINES + -0.11 * SECONDARY ROADS (KMS) + 0.14 * NUMBER OF INTERNATIONAL BORDER GATES + 0.14 * Density of restaurants and other food services per 100,000 inhabitants + 0.11 * Density of People employed in restaurants and the like per 10,000 inhabitants + 0.05 * Car rental companies + 0.11 * Densidad de camas en hospitales por cada 10.000 habitantes + 0.19 * Density of beds in hospitals per 10,000 inhabitants + -0.14 * Number of spas + 0.17 * Density of gambling casinos per million inhabitants + 0.03 * Number of golf courses + 0.03 * Number of craft centers + 0.18 * Density of tour guides per 100,000 inhabitants + -0.11 * Number of thermal centers + 0.00 * Density of Sports Facilities and Venues per 10,000 inhabitants + 0.19 * Penetration of telephone lines in service per 100 inhabitants + -0.02 * Density of service stations + 0.06 * Number of tour-operator companies certified with the tourism quality seal + 0.08 * Perception of exposure to crime (%) + -0.00 * Percentage of victimized households with at least one victim + 0.04 * Density of homicides per million inhabitants + 0.11 * Density of crimes against public health per million inhabitants + 0.04 * Black figure index + -0.01 * Budget for public safety (Thousands of $) + -0.08 * Percentage of households that reported at least one crime + 0.03 * Number of declared crimes + 0.02 * Number of crimes investigated + 0.02 * Number of accidents (roads, air and waterways) + 0.08 * Illegal commerce + 0.01 * Number of Carabineros + -0.09 * Unemployment rate + -0.20 * Poverty rate + 0.06 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + 0.06 * Number of strikes carried out + -0.03 * Average (days) duration of a strike + 0.07 * Person-day cost of a strike + 0.04 * Density of Bank Branches per million inhabitants + 0.06 * Floating population + 0.05 * Volume of exports + 0.19 * Density of Tourist Information Offices per million inhabitants + 0.04 * Number of visits to Tourist Information Offices + 0.03 * Average monthly global searches by tourist attraction on the internet + -0.03 * National tourism promotion budget (Thousands of USD) + 0.20 * International tourism promotion budget (Thousands of USD) + -0.03 * Investments in public infrastructure made by the Ministry of Public Works + -0.03 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.09 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + -0.05 * Funds obtained from FNRD (Thousands of pesos) + -0.10 * Number of regional strategic development plans 9.1%: -0.06 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + 0.05 * NUMBER OF CULTURAL CENTERS + -0.00 * WORLD CULTURAL HERITAGE SITES + -0.14 * NUMBER OF ARCHEOLOGICAL SITES + 0.01 * NATIONAL MONUMENTS + -0.01 * MUSEUMS + -0.09 * % OF POPULATION THAT ATTENDS MUSEUMS + 0.02 * THEATERS + 0.01 * NUMBER OF THEATER PLAYS PER YEAR + 0.06 * LIBRARIES + 0.02 * GALERIES + 0.17 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + 0.02 * NUMBER OF EXHIBITS + 0.01 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + 0.07 * MAJOR SPORTS EVENTS PER YEAR + -0.08 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + 0.02 * ARTWORK SITES + -0.05 * POPULAR ARCHITECTURE SITES + -0.08 * HISTORICAL SITES + 0.14 * LOCAL MARKETS + -0.03 * CULTURAL SITES LEVEL III (INTERNATIONAL) + 0.01 * CULTURA SITES LEVEL II (NATIONAL) + 0.04 * CULTURAL SITES LEVEL I (LOCAL) + -0.01 * HERITAGE ARCHITECTURAL HOUSES + 0.21 * % OF LAND THAT CORRESPONDS TO FORESTS + 0.13 * NATIONAL PROTECTED SITES (%) + 0.01 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + -0.09 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + 0.03 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.12 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + -0.04 * NUMBER OF BEACHES AND BEACH RESORTS + -0.09 * LAND AFFECTED BY WILDFIRES + 0.18 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + 0.18 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + 0.20 * RIVERS, LAKES AND WATERFALLS + 0.10 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + 0.14 * GEISERS AND THERMAL CENTERS + -0.03 * PIERS AND SEASHORES + 0.15 * GLACIERS AND WINTER VACATION LOCATIONS + 0.00 * VALLEYS + -0.20 * DESERTS AND DUNES + 0.15 * ISLANDS AND PENINSULAS + -0.05 * PALEONTOLOGY SITES + 0.08 * HIKING TRAILS + 0.20 * PRESERVED SITES + 0.08 * SEASHORE PROTECTED SITES + 0.16 * BIOSHPERE RESERVES + -0.04 * % AVAILABLE WORKFORCE + 0.17 * % POPULATION ORIENTED TOWARDS TOURISM + -0.04 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + 0.16 * 5 POPULATION WITH PRIMARY EDUCATION + -0.01 * % POPULATION WITH SECONDARY EDUCATION + -0.16 * AVERAGE NUMBER OF YEARS STUDYING + -0.00 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + 0.04 * TOURISM-ORIENTED INSTITUTIONS + 0.02 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + 0.02 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + 0.08 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + 0.03 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + -0.13 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + 0.07 * ROOMS PER 1000 HABITANTS + 0.10 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + -0.02 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + -0.05 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + -0.04 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + 0.19 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + 0.01 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + 0.00 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + -0.03 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + -0.04 * NATIONAL TOURISTS ARRIVALS + 0.03 * INTERNATIONAL TOURISTS ARRIVALS + 0.03 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + 0.08 * DENSITY OF AIRPORTS + -0.06 * DENSITY OF ROADS AND HIGHWAYS + 0.04 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + 0.01 * NUMBER OF VEHICLES + 0.16 * VISITORS TO PROTECTED SITES + 0.13 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + 0.01 * TOURIST'S ARRIVALS THROUGH BORDER LINES + 0.14 * SECONDARY ROADS (KMS) + 0.09 * NUMBER OF INTERNATIONAL BORDER GATES + 0.05 * Density of restaurants and other food services per 100,000 inhabitants + 0.01 * Density of People employed in restaurants and the like per 10,000 inhabitants + 0.10 * Car rental companies + 0.09 * Densidad de camas en hospitales por cada 10.000 habitantes + -0.08 * Density of beds in hospitals per 10,000 inhabitants + 0.10 * Number of spas + 0.07 * Density of gambling casinos per million inhabitants + -0.01 * Number of golf courses + 0.09 * Number of craft centers + 0.08 * Density of tour guides per 100,000 inhabitants + 0.14 * Number of thermal centers + -0.03 * Density of Sports Facilities and Venues per 10,000 inhabitants + -0.03 * Penetration of telephone lines in service per 100 inhabitants + 0.03 * Density of service stations + 0.04 * Number of tour-operator companies certified with the tourism quality seal + -0.08 * Perception of exposure to crime (%) + -0.11 * Percentage of victimized households with at least one victim + 0.06 * Density of homicides per million inhabitants + 0.05 * Density of crimes against public health per million inhabitants + -0.16 * Black figure index + 0.04 * Budget for public safety (Thousands of $) + -0.08 * Percentage of households that reported at least one crime + 0.01 * Number of declared crimes + 0.04 * Number of crimes investigated + 0.01 * Number of accidents (roads, air and waterways) + 0.02 * Illegal commerce + 0.03 * Number of Carabineros + -0.06 * Unemployment rate + 0.02 * Poverty rate + -0.11 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.00 * Number of strikes carried out + -0.04 * Average (days) duration of a strike + -0.07 * Person-day cost of a strike + 0.11 * Density of Bank Branches per million inhabitants + 0.02 * Floating population + -0.12 * Volume of exports + 0.07 * Density of Tourist Information Offices per million inhabitants + -0.10 * Number of visits to Tourist Information Offices + 0.06 * Average monthly global searches by tourist attraction on the internet + 0.06 * National tourism promotion budget (Thousands of USD) + 0.08 * International tourism promotion budget (Thousands of USD) + 0.06 * Investments in public infrastructure made by the Ministry of Public Works + 0.12 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + 0.08 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + 0.07 * Funds obtained from FNRD (Thousands of pesos) + 0.00 * Number of regional strategic development plans 8.5%: 0.05 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + 0.04 * NUMBER OF CULTURAL CENTERS + 0.23 * WORLD CULTURAL HERITAGE SITES + 0.12 * NUMBER OF ARCHEOLOGICAL SITES + 0.01 * NATIONAL MONUMENTS + -0.03 * MUSEUMS + 0.13 * % OF POPULATION THAT ATTENDS MUSEUMS + -0.06 * THEATERS + -0.07 * NUMBER OF THEATER PLAYS PER YEAR + -0.01 * LIBRARIES + -0.02 * GALERIES + -0.07 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + -0.07 * NUMBER OF EXHIBITS + 0.03 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + 0.04 * MAJOR SPORTS EVENTS PER YEAR + 0.17 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.03 * ARTWORK SITES + 0.21 * POPULAR ARCHITECTURE SITES + 0.06 * HISTORICAL SITES + -0.00 * LOCAL MARKETS + 0.25 * CULTURAL SITES LEVEL III (INTERNATIONAL) + 0.17 * CULTURA SITES LEVEL II (NATIONAL) + -0.03 * CULTURAL SITES LEVEL I (LOCAL) + 0.24 * HERITAGE ARCHITECTURAL HOUSES + -0.08 * % OF LAND THAT CORRESPONDS TO FORESTS + -0.01 * NATIONAL PROTECTED SITES (%) + -0.00 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + -0.05 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + 0.10 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.06 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + 0.22 * NUMBER OF BEACHES AND BEACH RESORTS + 0.26 * LAND AFFECTED BY WILDFIRES + 0.06 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + 0.10 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.08 * RIVERS, LAKES AND WATERFALLS + -0.11 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.10 * GEISERS AND THERMAL CENTERS + 0.18 * PIERS AND SEASHORES + -0.02 * GLACIERS AND WINTER VACATION LOCATIONS + 0.23 * VALLEYS + -0.05 * DESERTS AND DUNES + 0.03 * ISLANDS AND PENINSULAS + -0.11 * PALEONTOLOGY SITES + 0.07 * HIKING TRAILS + 0.01 * PRESERVED SITES + 0.24 * SEASHORE PROTECTED SITES + 0.17 * BIOSHPERE RESERVES + 0.03 * % AVAILABLE WORKFORCE + -0.00 * % POPULATION ORIENTED TOWARDS TOURISM + 0.00 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + 0.01 * 5 POPULATION WITH PRIMARY EDUCATION + 0.04 * % POPULATION WITH SECONDARY EDUCATION + 0.05 * AVERAGE NUMBER OF YEARS STUDYING + -0.00 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + 0.07 * TOURISM-ORIENTED INSTITUTIONS + -0.01 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + -0.02 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + 0.01 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + -0.02 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + -0.04 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + 0.03 * ROOMS PER 1000 HABITANTS + 0.14 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + 0.03 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + -0.09 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + 0.05 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + 0.06 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + -0.02 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + 0.19 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + 0.15 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + 0.07 * NATIONAL TOURISTS ARRIVALS + -0.03 * INTERNATIONAL TOURISTS ARRIVALS + 0.01 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + 0.02 * DENSITY OF AIRPORTS + -0.10 * DENSITY OF ROADS AND HIGHWAYS + 0.01 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + -0.03 * NUMBER OF VEHICLES + 0.06 * VISITORS TO PROTECTED SITES + 0.21 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + 0.03 * TOURIST'S ARRIVALS THROUGH BORDER LINES + -0.05 * SECONDARY ROADS (KMS) + -0.04 * NUMBER OF INTERNATIONAL BORDER GATES + 0.12 * Density of restaurants and other food services per 100,000 inhabitants + 0.16 * Density of People employed in restaurants and the like per 10,000 inhabitants + -0.05 * Car rental companies + -0.02 * Densidad de camas en hospitales por cada 10.000 habitantes + 0.05 * Density of beds in hospitals per 10,000 inhabitants + 0.03 * Number of spas + 0.01 * Density of gambling casinos per million inhabitants + 0.06 * Number of golf courses + 0.05 * Number of craft centers + 0.01 * Density of tour guides per 100,000 inhabitants + -0.09 * Number of thermal centers + -0.02 * Density of Sports Facilities and Venues per 10,000 inhabitants + 0.02 * Penetration of telephone lines in service per 100 inhabitants + -0.02 * Density of service stations + -0.05 * Number of tour-operator companies certified with the tourism quality seal + -0.06 * Perception of exposure to crime (%) + -0.03 * Percentage of victimized households with at least one victim + 0.06 * Density of homicides per million inhabitants + 0.00 * Density of crimes against public health per million inhabitants + -0.01 * Black figure index + -0.07 * Budget for public safety (Thousands of $) + -0.03 * Percentage of households that reported at least one crime + -0.04 * Number of declared crimes + -0.01 * Number of crimes investigated + -0.02 * Number of accidents (roads, air and waterways) + -0.07 * Illegal commerce + -0.03 * Number of Carabineros + -0.01 * Unemployment rate + -0.04 * Poverty rate + -0.01 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.06 * Number of strikes carried out + -0.01 * Average (days) duration of a strike + -0.06 * Person-day cost of a strike + -0.04 * Density of Bank Branches per million inhabitants + -0.01 * Floating population + 0.01 * Volume of exports + -0.01 * Density of Tourist Information Offices per million inhabitants + -0.09 * Number of visits to Tourist Information Offices + -0.00 * Average monthly global searches by tourist attraction on the internet + 0.00 * National tourism promotion budget (Thousands of USD) + -0.00 * International tourism promotion budget (Thousands of USD) + -0.02 * Investments in public infrastructure made by the Ministry of Public Works + 0.01 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + 0.04 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + -0.03 * Funds obtained from FNRD (Thousands of pesos) + -0.05 * Number of regional strategic development plans 6.8%: -0.17 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + 0.03 * NUMBER OF CULTURAL CENTERS + 0.05 * WORLD CULTURAL HERITAGE SITES + -0.03 * NUMBER OF ARCHEOLOGICAL SITES + -0.04 * NATIONAL MONUMENTS + 0.06 * MUSEUMS + 0.03 * % OF POPULATION THAT ATTENDS MUSEUMS + -0.04 * THEATERS + -0.04 * NUMBER OF THEATER PLAYS PER YEAR + -0.05 * LIBRARIES + -0.04 * GALERIES + -0.05 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + -0.02 * NUMBER OF EXHIBITS + -0.15 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + -0.14 * MAJOR SPORTS EVENTS PER YEAR + 0.02 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.19 * ARTWORK SITES + 0.08 * POPULAR ARCHITECTURE SITES + 0.07 * HISTORICAL SITES + -0.09 * LOCAL MARKETS + -0.05 * CULTURAL SITES LEVEL III (INTERNATIONAL) + 0.02 * CULTURA SITES LEVEL II (NATIONAL) + 0.01 * CULTURAL SITES LEVEL I (LOCAL) + -0.05 * HERITAGE ARCHITECTURAL HOUSES + 0.09 * % OF LAND THAT CORRESPONDS TO FORESTS + -0.09 * NATIONAL PROTECTED SITES (%) + -0.07 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + 0.00 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + -0.12 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + 0.02 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + 0.08 * NUMBER OF BEACHES AND BEACH RESORTS + -0.05 * LAND AFFECTED BY WILDFIRES + -0.11 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.09 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.07 * RIVERS, LAKES AND WATERFALLS + 0.01 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.01 * GEISERS AND THERMAL CENTERS + 0.11 * PIERS AND SEASHORES + -0.14 * GLACIERS AND WINTER VACATION LOCATIONS + -0.07 * VALLEYS + 0.09 * DESERTS AND DUNES + 0.26 * ISLANDS AND PENINSULAS + 0.08 * PALEONTOLOGY SITES + -0.16 * HIKING TRAILS + 0.17 * PRESERVED SITES + 0.14 * SEASHORE PROTECTED SITES + -0.06 * BIOSHPERE RESERVES + 0.11 * % AVAILABLE WORKFORCE + 0.21 * % POPULATION ORIENTED TOWARDS TOURISM + -0.00 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.19 * 5 POPULATION WITH PRIMARY EDUCATION + -0.16 * % POPULATION WITH SECONDARY EDUCATION + -0.03 * AVERAGE NUMBER OF YEARS STUDYING + -0.03 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + 0.01 * TOURISM-ORIENTED INSTITUTIONS + -0.02 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + -0.03 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + -0.08 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + -0.03 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + 0.02 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + 0.03 * ROOMS PER 1000 HABITANTS + 0.08 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + 0.01 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + 0.07 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + -0.09 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + 0.12 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + -0.03 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + -0.03 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + 0.04 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + 0.14 * NATIONAL TOURISTS ARRIVALS + -0.02 * INTERNATIONAL TOURISTS ARRIVALS + -0.13 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + 0.20 * DENSITY OF AIRPORTS + 0.06 * DENSITY OF ROADS AND HIGHWAYS + -0.00 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + -0.03 * NUMBER OF VEHICLES + 0.16 * VISITORS TO PROTECTED SITES + 0.05 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + -0.03 * TOURIST'S ARRIVALS THROUGH BORDER LINES + 0.16 * SECONDARY ROADS (KMS) + 0.01 * NUMBER OF INTERNATIONAL BORDER GATES + -0.01 * Density of restaurants and other food services per 100,000 inhabitants + -0.07 * Density of People employed in restaurants and the like per 10,000 inhabitants + 0.02 * Car rental companies + -0.06 * Densidad de camas en hospitales por cada 10.000 habitantes + 0.05 * Density of beds in hospitals per 10,000 inhabitants + -0.08 * Number of spas + -0.05 * Density of gambling casinos per million inhabitants + -0.02 * Number of golf courses + -0.06 * Number of craft centers + -0.08 * Density of tour guides per 100,000 inhabitants + 0.02 * Number of thermal centers + 0.11 * Density of Sports Facilities and Venues per 10,000 inhabitants + -0.02 * Penetration of telephone lines in service per 100 inhabitants + -0.04 * Density of service stations + -0.04 * Number of tour-operator companies certified with the tourism quality seal + 0.14 * Perception of exposure to crime (%) + 0.06 * Percentage of victimized households with at least one victim + 0.13 * Density of homicides per million inhabitants + -0.17 * Density of crimes against public health per million inhabitants + -0.03 * Black figure index + -0.02 * Budget for public safety (Thousands of $) + -0.04 * Percentage of households that reported at least one crime + -0.03 * Number of declared crimes + -0.02 * Number of crimes investigated + -0.04 * Number of accidents (roads, air and waterways) + -0.07 * Illegal commerce + -0.02 * Number of Carabineros + -0.17 * Unemployment rate + 0.04 * Poverty rate + 0.05 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.03 * Number of strikes carried out + -0.03 * Average (days) duration of a strike + 0.00 * Person-day cost of a strike + 0.12 * Density of Bank Branches per million inhabitants + -0.05 * Floating population + 0.13 * Volume of exports + -0.04 * Density of Tourist Information Offices per million inhabitants + 0.05 * Number of visits to Tourist Information Offices + 0.11 * Average monthly global searches by tourist attraction on the internet + 0.10 * National tourism promotion budget (Thousands of USD) + -0.07 * International tourism promotion budget (Thousands of USD) + 0.02 * Investments in public infrastructure made by the Ministry of Public Works + 0.15 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + 0.06 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + 0.08 * Funds obtained from FNRD (Thousands of pesos) + 0.14 * Number of regional strategic development plans 5.1%: 0.08 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + -0.04 * NUMBER OF CULTURAL CENTERS + 0.12 * WORLD CULTURAL HERITAGE SITES + 0.06 * NUMBER OF ARCHEOLOGICAL SITES + 0.01 * NATIONAL MONUMENTS + -0.09 * MUSEUMS + -0.06 * % OF POPULATION THAT ATTENDS MUSEUMS + 0.01 * THEATERS + 0.03 * NUMBER OF THEATER PLAYS PER YEAR + -0.04 * LIBRARIES + -0.00 * GALERIES + 0.12 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + -0.01 * NUMBER OF EXHIBITS + -0.04 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + -0.03 * MAJOR SPORTS EVENTS PER YEAR + -0.07 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.03 * ARTWORK SITES + 0.09 * POPULAR ARCHITECTURE SITES + -0.24 * HISTORICAL SITES + 0.05 * LOCAL MARKETS + 0.02 * CULTURAL SITES LEVEL III (INTERNATIONAL) + 0.09 * CULTURA SITES LEVEL II (NATIONAL) + -0.05 * CULTURAL SITES LEVEL I (LOCAL) + -0.03 * HERITAGE ARCHITECTURAL HOUSES + 0.00 * % OF LAND THAT CORRESPONDS TO FORESTS + 0.01 * NATIONAL PROTECTED SITES (%) + 0.03 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + 0.25 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + -0.02 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.08 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + -0.09 * NUMBER OF BEACHES AND BEACH RESORTS + -0.01 * LAND AFFECTED BY WILDFIRES + -0.01 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.09 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.12 * RIVERS, LAKES AND WATERFALLS + -0.12 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + 0.12 * GEISERS AND THERMAL CENTERS + -0.16 * PIERS AND SEASHORES + -0.09 * GLACIERS AND WINTER VACATION LOCATIONS + -0.02 * VALLEYS + -0.06 * DESERTS AND DUNES + 0.09 * ISLANDS AND PENINSULAS + -0.22 * PALEONTOLOGY SITES + -0.02 * HIKING TRAILS + 0.13 * PRESERVED SITES + 0.01 * SEASHORE PROTECTED SITES + 0.04 * BIOSHPERE RESERVES + 0.08 * % AVAILABLE WORKFORCE + 0.12 * % POPULATION ORIENTED TOWARDS TOURISM + -0.12 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + 0.06 * 5 POPULATION WITH PRIMARY EDUCATION + 0.04 * % POPULATION WITH SECONDARY EDUCATION + 0.07 * AVERAGE NUMBER OF YEARS STUDYING + 0.03 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + -0.05 * TOURISM-ORIENTED INSTITUTIONS + -0.01 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + -0.01 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + -0.07 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + 0.03 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + 0.08 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + 0.00 * ROOMS PER 1000 HABITANTS + -0.04 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + 0.01 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + 0.00 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + 0.07 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + 0.10 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + 0.03 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + -0.00 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + -0.02 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + -0.11 * NATIONAL TOURISTS ARRIVALS + 0.03 * INTERNATIONAL TOURISTS ARRIVALS + 0.18 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + 0.23 * DENSITY OF AIRPORTS + 0.02 * DENSITY OF ROADS AND HIGHWAYS + -0.08 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + 0.00 * NUMBER OF VEHICLES + -0.09 * VISITORS TO PROTECTED SITES + 0.07 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + 0.09 * TOURIST'S ARRIVALS THROUGH BORDER LINES + -0.01 * SECONDARY ROADS (KMS) + -0.12 * NUMBER OF INTERNATIONAL BORDER GATES + -0.03 * Density of restaurants and other food services per 100,000 inhabitants + -0.08 * Density of People employed in restaurants and the like per 10,000 inhabitants + -0.07 * Car rental companies + -0.24 * Densidad de camas en hospitales por cada 10.000 habitantes + -0.07 * Density of beds in hospitals per 10,000 inhabitants + -0.08 * Number of spas + -0.02 * Density of gambling casinos per million inhabitants + -0.01 * Number of golf courses + -0.05 * Number of craft centers + -0.07 * Density of tour guides per 100,000 inhabitants + 0.11 * Number of thermal centers + 0.01 * Density of Sports Facilities and Venues per 10,000 inhabitants + -0.00 * Penetration of telephone lines in service per 100 inhabitants + -0.03 * Density of service stations + 0.06 * Number of tour-operator companies certified with the tourism quality seal + -0.09 * Perception of exposure to crime (%) + 0.11 * Percentage of victimized households with at least one victim + -0.03 * Density of homicides per million inhabitants + 0.13 * Density of crimes against public health per million inhabitants + 0.19 * Black figure index + -0.11 * Budget for public safety (Thousands of $) + -0.15 * Percentage of households that reported at least one crime + -0.01 * Number of declared crimes + -0.02 * Number of crimes investigated + -0.01 * Number of accidents (roads, air and waterways) + 0.05 * Illegal commerce + -0.01 * Number of Carabineros + -0.11 * Unemployment rate + 0.00 * Poverty rate + 0.22 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + 0.01 * Number of strikes carried out + -0.08 * Average (days) duration of a strike + 0.06 * Person-day cost of a strike + 0.09 * Density of Bank Branches per million inhabitants + 0.02 * Floating population + -0.18 * Volume of exports + -0.01 * Density of Tourist Information Offices per million inhabitants + -0.10 * Number of visits to Tourist Information Offices + 0.04 * Average monthly global searches by tourist attraction on the internet + -0.12 * National tourism promotion budget (Thousands of USD) + -0.10 * International tourism promotion budget (Thousands of USD) + -0.01 * Investments in public infrastructure made by the Ministry of Public Works + -0.09 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.09 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + -0.12 * Funds obtained from FNRD (Thousands of pesos) + -0.05 * Number of regional strategic development plans 4.7%: -0.07 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + -0.01 * NUMBER OF CULTURAL CENTERS + 0.06 * WORLD CULTURAL HERITAGE SITES + 0.17 * NUMBER OF ARCHEOLOGICAL SITES + 0.04 * NATIONAL MONUMENTS + -0.01 * MUSEUMS + 0.05 * % OF POPULATION THAT ATTENDS MUSEUMS + -0.04 * THEATERS + -0.04 * NUMBER OF THEATER PLAYS PER YEAR + 0.02 * LIBRARIES + 0.01 * GALERIES + 0.16 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + -0.05 * NUMBER OF EXHIBITS + 0.02 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + 0.16 * MAJOR SPORTS EVENTS PER YEAR + -0.13 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + 0.03 * ARTWORK SITES + -0.08 * POPULAR ARCHITECTURE SITES + 0.16 * HISTORICAL SITES + 0.23 * LOCAL MARKETS + 0.12 * CULTURAL SITES LEVEL III (INTERNATIONAL) + 0.03 * CULTURA SITES LEVEL II (NATIONAL) + -0.02 * CULTURAL SITES LEVEL I (LOCAL) + 0.01 * HERITAGE ARCHITECTURAL HOUSES + -0.01 * % OF LAND THAT CORRESPONDS TO FORESTS + -0.02 * NATIONAL PROTECTED SITES (%) + -0.04 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + 0.18 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + 0.07 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.04 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + 0.10 * NUMBER OF BEACHES AND BEACH RESORTS + 0.04 * LAND AFFECTED BY WILDFIRES + 0.13 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + 0.10 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + 0.14 * RIVERS, LAKES AND WATERFALLS + 0.13 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + 0.19 * GEISERS AND THERMAL CENTERS + -0.03 * PIERS AND SEASHORES + -0.08 * GLACIERS AND WINTER VACATION LOCATIONS + 0.04 * VALLEYS + 0.13 * DESERTS AND DUNES + -0.06 * ISLANDS AND PENINSULAS + 0.05 * PALEONTOLOGY SITES + 0.22 * HIKING TRAILS + -0.07 * PRESERVED SITES + -0.03 * SEASHORE PROTECTED SITES + -0.00 * BIOSHPERE RESERVES + 0.02 * % AVAILABLE WORKFORCE + 0.00 * % POPULATION ORIENTED TOWARDS TOURISM + -0.00 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.10 * 5 POPULATION WITH PRIMARY EDUCATION + 0.07 * % POPULATION WITH SECONDARY EDUCATION + 0.09 * AVERAGE NUMBER OF YEARS STUDYING + -0.02 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + 0.00 * TOURISM-ORIENTED INSTITUTIONS + -0.03 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + -0.03 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + -0.02 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + -0.07 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + 0.04 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + -0.01 * ROOMS PER 1000 HABITANTS + 0.06 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + 0.14 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + 0.14 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + 0.00 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + 0.11 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + -0.02 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + -0.01 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + -0.14 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + 0.13 * NATIONAL TOURISTS ARRIVALS + -0.01 * INTERNATIONAL TOURISTS ARRIVALS + 0.01 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + 0.06 * DENSITY OF AIRPORTS + 0.04 * DENSITY OF ROADS AND HIGHWAYS + -0.06 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + -0.04 * NUMBER OF VEHICLES + 0.14 * VISITORS TO PROTECTED SITES + 0.05 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + 0.06 * TOURIST'S ARRIVALS THROUGH BORDER LINES + 0.04 * SECONDARY ROADS (KMS) + 0.14 * NUMBER OF INTERNATIONAL BORDER GATES + -0.05 * Density of restaurants and other food services per 100,000 inhabitants + -0.07 * Density of People employed in restaurants and the like per 10,000 inhabitants + 0.08 * Car rental companies + 0.08 * Densidad de camas en hospitales por cada 10.000 habitantes + 0.10 * Density of beds in hospitals per 10,000 inhabitants + 0.25 * Number of spas + -0.04 * Density of gambling casinos per million inhabitants + 0.00 * Number of golf courses + -0.11 * Number of craft centers + -0.02 * Density of tour guides per 100,000 inhabitants + 0.15 * Number of thermal centers + 0.11 * Density of Sports Facilities and Venues per 10,000 inhabitants + 0.08 * Penetration of telephone lines in service per 100 inhabitants + -0.04 * Density of service stations + -0.02 * Number of tour-operator companies certified with the tourism quality seal + 0.25 * Perception of exposure to crime (%) + 0.19 * Percentage of victimized households with at least one victim + -0.09 * Density of homicides per million inhabitants + -0.02 * Density of crimes against public health per million inhabitants + 0.06 * Black figure index + -0.00 * Budget for public safety (Thousands of $) + 0.02 * Percentage of households that reported at least one crime + -0.02 * Number of declared crimes + -0.02 * Number of crimes investigated + -0.02 * Number of accidents (roads, air and waterways) + -0.05 * Illegal commerce + -0.03 * Number of Carabineros + 0.11 * Unemployment rate + 0.11 * Poverty rate + 0.13 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.03 * Number of strikes carried out + 0.09 * Average (days) duration of a strike + 0.02 * Person-day cost of a strike + -0.01 * Density of Bank Branches per million inhabitants + -0.02 * Floating population + 0.10 * Volume of exports + -0.03 * Density of Tourist Information Offices per million inhabitants + 0.07 * Number of visits to Tourist Information Offices + -0.05 * Average monthly global searches by tourist attraction on the internet + -0.11 * National tourism promotion budget (Thousands of USD) + -0.06 * International tourism promotion budget (Thousands of USD) + -0.05 * Investments in public infrastructure made by the Ministry of Public Works + -0.00 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.09 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + -0.01 * Funds obtained from FNRD (Thousands of pesos) + -0.08 * Number of regional strategic development plans Total variance explained by the 7 components 80.9%
In [104]:
# Calculate factor scores
pca_model = myPCA.fit_transform(chile_data_s)
PCcomponents = pd.DataFrame(data = pca_model, columns = ['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7'])
print("\n The Factor scores are")
PCcomponents
The Factor scores are
Out[104]:
| PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | |
|---|---|---|---|---|---|---|---|
| 0 | -3.353498 | 1.608181 | -1.582183 | -0.237261 | -3.449640 | 4.862151 | 0.772824 |
| 1 | -2.164654 | 1.660241 | -5.227525 | -1.162747 | 2.753981 | 4.826127 | 2.701062 |
| 2 | -0.727422 | 3.703057 | -3.630520 | -0.381449 | 3.291641 | -4.412543 | 2.910755 |
| 3 | -3.479076 | 0.821718 | -4.731121 | -1.918471 | 1.215268 | -1.226846 | 0.481672 |
| 4 | -0.830247 | -3.134555 | -2.097779 | 3.468263 | 1.943877 | -0.477330 | -3.890678 |
| 5 | 4.588837 | -1.055439 | -0.086863 | 10.589977 | -1.359019 | -0.558769 | 1.995606 |
| 6 | 21.826574 | 4.335464 | 0.555220 | -2.699925 | -1.391621 | 0.903598 | -0.918454 |
| 7 | -1.280227 | -4.059490 | -1.926913 | 0.722814 | -3.125349 | 0.505362 | -3.254364 |
| 8 | -1.093260 | -5.499579 | -0.538222 | -1.896991 | -2.065633 | -0.431173 | -0.772332 |
| 9 | 3.139942 | -3.838975 | -0.460303 | -2.765644 | 2.092178 | -3.496457 | -1.312946 |
| 10 | -0.565803 | -6.063523 | 4.890623 | -2.249734 | -2.930731 | -0.981432 | 5.700374 |
| 11 | -2.827197 | -2.137913 | 1.260431 | -2.341001 | 0.224917 | 0.360441 | -1.389120 |
| 12 | -0.385161 | -0.194387 | 7.225696 | 1.156990 | 7.286542 | 2.600561 | -0.380118 |
| 13 | -6.999638 | 4.791132 | 3.947399 | -1.193484 | -1.698133 | 0.113016 | -2.335758 |
| 14 | -5.849170 | 9.064066 | 2.402060 | 0.908662 | -2.788278 | -2.586707 | -0.308524 |
In [105]:
# Example of different variables in each component
# Fit the model
myPCA = PCA(n_components = 7)
pca_model = myPCA.fit(chile_data_s)
y_axis = [0,0,0,0,0,0,0]
for i in range(0,7):
y_axis[i]=[np.mean(pca_model.components_[i][0:24]), np.mean(pca_model.components_[i][24:47]),
np.mean(pca_model.components_[i][47:59]), np.mean(pca_model.components_[i][59:69]),
np.mean(pca_model.components_[i][69:81]), np.mean(pca_model.components_[i][81:96]),
np.mean(pca_model.components_[i][96:108]), np.mean(pca_model.components_[i][108:117]),
np.mean(pca_model.components_[i][117:122]), np.mean(pca_model.components_[i][122:127])]
# Plot
x_axis = ['CULTURAL HERITAGE', 'NATURAL RESOURCES', 'WORKFORCE DEVELOPMENT', 'TOURISM INFRASTRUCTURE', 'TOURISM MOBILITY',
'TOURISM-RELATED SERVICES', 'SECURITY AND SAFETY ', 'ECONOMIC PERFORMANCE', 'TOURISM PROMOTION',
'GOVERNMENTAL INVOLVEMENT AND EFFICIENCY']
plt.plot(x_axis,y_axis[0], color = 'mediumaquamarine', label = "C1")
plt.plot(x_axis,y_axis[1], color = 'yellow', label = "C2")
plt.plot(x_axis,y_axis[2], color = 'pink', label = "C3")
plt.plot(x_axis,y_axis[3], color = 'steelblue', label = "C4")
plt.plot(x_axis,y_axis[4], color = 'salmon', label = "C5")
plt.plot(x_axis,y_axis[5], color = 'red', label = "C6")
plt.plot(x_axis,y_axis[6], color = 'orange', label = "C7")
plt.xticks(rotation = 90)
plt.title('Example of variable contributions to each principal component')
plt.legend()
pass
5. Developing a scoring system for 10 dimensions
Step 1 - Calculate a weighted average for each variable in principal components.
Multiply the percentage value of the explained variance by the percentage value of a feature in the selected principal component. As a result, a weighted average will be a new column in the dataframe with principal components.
In [106]:
# Creating a dataframe of weights
weights = pd.DataFrame(np.column_stack((chile_data_s.columns, pca_model.components_[0] *
pca_model.explained_variance_ratio_[0],
pca_model.components_[1] * pca_model.explained_variance_ratio_[1],
pca_model.components_[2] * pca_model.explained_variance_ratio_[2],
pca_model.components_[3] * pca_model.explained_variance_ratio_[3],
pca_model.components_[4] * pca_model.explained_variance_ratio_[4],
pca_model.components_[5] * pca_model.explained_variance_ratio_[5],
pca_model.components_[6] * pca_model.explained_variance_ratio_[6])))
weights = weights.set_index(0)
# Create a weighted average
weights['weighted_average'] = weights.sum(axis = 1)/np.sum(pca_model.explained_variance_ratio_)
# Print
weights.head()
Out[106]:
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | weighted_average | |
|---|---|---|---|---|---|---|---|---|
| 0 | ||||||||
| CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR | -0.00338617 | -0.0193029 | -0.00526498 | 0.00454266 | -0.0119294 | 0.00416171 | -0.00309488 | -0.042377 |
| NUMBER OF CULTURAL CENTERS | 0.0467218 | -0.00324475 | 0.00452202 | 0.00358209 | 0.00175027 | -0.0020978 | -0.000501406 | 0.062727 |
| WORLD CULTURAL HERITAGE SITES | 0.00470774 | -0.00414158 | -9.02077e-05 | 0.0192769 | 0.00369491 | 0.0059991 | 0.0026919 | 0.039737 |
| NUMBER OF ARCHEOLOGICAL SITES | -0.00454166 | 0.0063454 | -0.0130138 | 0.0105897 | -0.00207997 | 0.00303106 | 0.00804497 | 0.010356 |
| NATIONAL MONUMENTS | 0.0490703 | 0.00539749 | 0.00102575 | 0.00108393 | -0.00243871 | 0.000643127 | 0.00175062 | 0.069898 |
In [107]:
# Ranking for dimension 1: CULTURAL HERITAGE AND EVENTS
# Create a dataframe for relevant variables
dim1 = chile_data_s.iloc[:, 0:24].mul(weights['weighted_average'][0:24], axis = 1)
# Create a score ranking
dim1['Ranking 1'] = dim1.sum(axis = 1)
# Sort by score
dim1.sort_values(by = 'Ranking 1', ascending = False).head()
Out[107]:
| CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR | NUMBER OF CULTURAL CENTERS | WORLD CULTURAL HERITAGE SITES | NUMBER OF ARCHEOLOGICAL SITES | NATIONAL MONUMENTS | MUSEUMS | % OF POPULATION THAT ATTENDS MUSEUMS | THEATERS | NUMBER OF THEATER PLAYS PER YEAR | LIBRARIES | GALERIES | % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP | NUMBER OF EXHIBITS | ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR | MAJOR SPORTS EVENTS PER YEAR | OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS | ARTWORK SITES | POPULAR ARCHITECTURE SITES | HISTORICAL SITES | LOCAL MARKETS | CULTURAL SITES LEVEL III (INTERNATIONAL) | CULTURA SITES LEVEL II (NATIONAL) | CULTURAL SITES LEVEL I (LOCAL) | HERITAGE ARCHITECTURAL HOUSES | Ranking 1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | |||||||||||||||||||||||||
| Metropolitana | 0.026107 | 0.170834 | -0.022214 | -0.007255 | 0.243608 | 0.173958 | 0.022132 | 0.202428 | 0.213861 | 0.166276 | 0.218911 | -0.007171 | 0.203508 | 0.026561 | 0.005815 | 0.011589 | 0.013684 | -0.004720 | -0.001878 | 0.017208 | -0.003481 | 0.167385 | 0.190988 | 0.040105 | 2.068241 |
| Valparaíso | -0.009820 | 0.089552 | 0.111069 | 0.015921 | 0.058271 | 0.014342 | 0.050767 | -0.009650 | -0.011451 | 0.039243 | 0.034780 | -0.007990 | -0.005682 | -0.021249 | -0.008723 | 0.018832 | 0.013684 | 0.004562 | 0.020656 | 0.017208 | 0.153156 | 0.184191 | 0.017501 | 0.157885 | 0.927056 |
| Biobío | 0.022514 | 0.068162 | -0.022214 | -0.008263 | -0.019525 | 0.069861 | -0.010082 | 0.007181 | -0.005453 | 0.051767 | 0.034780 | -0.007353 | 0.048553 | 0.026561 | 0.005815 | -0.010140 | -0.002737 | -0.002993 | 0.024412 | -0.002647 | -0.018399 | -0.084701 | 0.058590 | 0.003398 | 0.227089 |
| Los Lagos | 0.036885 | 0.033939 | 0.044428 | -0.008263 | -0.015712 | 0.007402 | -0.002028 | -0.006284 | -0.013258 | -0.005487 | -0.026597 | 0.008026 | -0.013429 | 0.026561 | 0.005815 | -0.010140 | 0.030106 | 0.002403 | -0.013145 | 0.007280 | -0.018399 | 0.073273 | 0.003805 | -0.007429 | 0.139753 |
| Antofagasta | 0.036885 | -0.030231 | -0.022214 | 0.019951 | -0.002746 | 0.042102 | 0.065979 | -0.006284 | -0.015751 | -0.034114 | -0.016367 | -0.005715 | -0.013429 | 0.010624 | 0.005815 | 0.011589 | 0.021895 | -0.000619 | 0.050702 | -0.022503 | -0.003481 | -0.014117 | -0.005326 | -0.015616 | 0.057032 |
In [108]:
# Ranking for dimension 2: NATURAL RESOURCES AND SUSTAINABILITY
# Create a dataframe for relevant variables
dim2 = chile_data_s.iloc[:, 24:47].mul(weights['weighted_average'][24:47], axis = 1)
# Create a score ranking
dim2['Ranking 2'] = dim2.sum(axis = 1)
# Sort the by score
dim2.sort_values(by = 'Ranking 2', ascending = False).head()
Out[108]:
| % OF LAND THAT CORRESPONDS TO FORESTS | NATIONAL PROTECTED SITES (%) | % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS | TOXIC WASTE DISPOSAL (TONS/100 hab.) | NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED | ENVIRONMENTAL ISSUES PER MILLION HABITANTS | NUMBER OF BEACHES AND BEACH RESORTS | LAND AFFECTED BY WILDFIRES | NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) | NATURAL PROTECTED SITES LEVEL II (NATIONAL) | RIVERS, LAKES AND WATERFALLS | MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS | GEISERS AND THERMAL CENTERS | PIERS AND SEASHORES | GLACIERS AND WINTER VACATION LOCATIONS | VALLEYS | DESERTS AND DUNES | ISLANDS AND PENINSULAS | PALEONTOLOGY SITES | HIKING TRAILS | PRESERVED SITES | SEASHORE PROTECTED SITES | BIOSHPERE RESERVES | Ranking 2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | ||||||||||||||||||||||||
| Valparaíso | -0.002588 | -0.002375 | 0.041800 | -0.004914 | 0.120449 | 0.015085 | 0.025177 | 0.045138 | 0.009270 | 0.009542 | 0.014837 | 0.017461 | -0.000444 | 0.005804 | 0.001470 | 0.067877 | 0.008466 | -0.020484 | 0.029040 | -0.012222 | -1.530539e-02 | 0.099920 | 0.042806 | 0.495811 |
| Metropolitana | -0.002518 | -0.002852 | 0.219560 | 0.015127 | 0.159164 | 0.015085 | -0.010703 | -0.009010 | -0.012457 | -0.008154 | 0.019230 | 0.008633 | -0.000269 | -0.007162 | 0.000134 | 0.004547 | 0.014239 | -0.020484 | 0.029040 | 0.005726 | -1.530539e-02 | -0.028549 | -0.021403 | 0.351619 |
| Los Lagos | 0.006125 | 0.001119 | -0.024946 | -0.013543 | -0.034414 | 0.015085 | 0.000243 | -0.009338 | 0.007098 | 0.001735 | -0.005662 | -0.011968 | 0.000607 | 0.002099 | 0.001470 | -0.010068 | 0.014239 | 0.121681 | 0.029040 | 0.005726 | 1.173413e-01 | 0.099920 | 0.010701 | 0.324290 |
| Coquimbo | -0.003585 | -0.002984 | -0.019067 | -0.035338 | -0.034414 | 0.015085 | 0.011798 | 0.002968 | -0.010284 | -0.004771 | 0.008981 | 0.017461 | -0.000794 | 0.012286 | 0.003474 | 0.048391 | 0.014239 | 0.020790 | 0.012906 | 0.005726 | -1.530539e-02 | 0.057097 | 0.010701 | 0.115361 |
| Arica y Parinacota | 0.000000 | 0.002707 | -0.015263 | 0.040780 | -0.034414 | 0.015085 | -0.007663 | 0.000000 | 0.009270 | -0.005552 | 0.013373 | -0.000196 | 0.000082 | -0.005310 | 0.003474 | -0.000325 | 0.002694 | -0.020484 | 0.029040 | 0.003162 | 5.890698e-17 | -0.028549 | 0.010701 | 0.012614 |
In [109]:
# Ranking for dimension 3: HUMAN RESOURCES AND TOURISM-RELATED WORKFORCE DEVELOPMENT
# Create a dataframe for relevant variables
dim3 = chile_data_s.iloc[:, 47:59].mul(weights['weighted_average'][47:59], axis = 1)
# Create a score ranking
dim3['Ranking 3'] = dim3.sum(axis = 1)
# Sort the dataframe by score
dim3.sort_values(by = 'Ranking 3', ascending = False).head()
Out[109]:
| % AVAILABLE WORKFORCE | % POPULATION ORIENTED TOWARDS TOURISM | AVERAGE MONTHLY INCOME (CHILEAN PESOS) | 5 POPULATION WITH PRIMARY EDUCATION | % POPULATION WITH SECONDARY EDUCATION | AVERAGE NUMBER OF YEARS STUDYING | HIGHER EDUCATION AND TECHNICAL INSTITUTIONS | TOURISM-ORIENTED INSTITUTIONS | NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS | AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS | DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) | CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS | Ranking 3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | |||||||||||||
| Metropolitana | 0.011089 | 0.068671 | 0.018237 | 0.000116 | 0.000051 | 0.073637 | 0.022265 | 0.191914 | 0.221282 | 0.226705 | -0.000839 | 0.213654 | 1.046783 |
| Valparaíso | 0.000365 | -0.021839 | -0.009772 | 0.001271 | -0.000280 | 0.037223 | 0.012478 | 0.110441 | 0.044006 | 0.032298 | -0.000526 | 0.022925 | 0.228590 |
| Los Lagos | 0.004991 | 0.227734 | -0.014812 | 0.001271 | 0.000469 | -0.053812 | 0.007585 | 0.010863 | -0.017382 | -0.017898 | -0.000156 | 0.006607 | 0.155459 |
| Biobío | -0.021759 | -0.017555 | -0.015082 | 0.002426 | -0.000015 | -0.005260 | 0.017372 | 0.074231 | 0.045474 | 0.033121 | -0.000976 | 0.001031 | 0.113008 |
| Tarapacá | 0.068459 | -0.013627 | 0.008067 | 0.008204 | 0.000469 | 0.043292 | -0.005872 | -0.034400 | -0.029311 | -0.028801 | -0.000103 | -0.031209 | -0.014831 |
In [110]:
# Ranking for dimension 4: TOURISM INFRASTRUCTURE
# Create a dataframe for relevant variables
dim4 = chile_data_s.iloc[:, 59:69].mul(weights['weighted_average'][59:69], axis = 1)
# Create a score ranking
dim4['Ranking 4'] = dim4.sum(axis = 1)
# Sort the dataframe by score
dim4.sort_values(by = 'Ranking 4', ascending = False).head()
Out[110]:
| % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR | ROOMS PER 1000 HABITANTS | NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. | TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) | AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR | AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND | NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION | NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS | TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS | TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) | Ranking 4 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | |||||||||||
| Metropolitana | 0.132687 | -0.005818 | 0.151380 | -0.012278 | 0.130533 | -0.002260 | 0.077979 | 0.241397 | 0.135150 | 0.014396 | 0.863165 |
| Valparaíso | -0.006067 | -0.001894 | 0.156990 | -0.001718 | -0.030317 | 0.000969 | 0.067628 | 0.021063 | 0.172205 | 0.016512 | 0.395370 |
| Los Lagos | -0.026826 | 0.002688 | 0.099246 | 0.000632 | -0.020164 | 0.004197 | 0.202194 | -0.008037 | -0.021318 | -0.004548 | 0.228063 |
| Coquimbo | -0.017408 | -0.000924 | 0.027021 | -0.009892 | -0.000926 | -0.011945 | -0.035884 | -0.016352 | 0.052390 | 0.066190 | 0.052269 |
| Araucanía | -0.045862 | -0.003406 | 0.038588 | -0.006250 | 0.010830 | -0.007103 | 0.077979 | -0.020509 | -0.028045 | -0.006866 | 0.009356 |
In [111]:
# Ranking for dimension 5: TOURISM MOBILITY AND TRANSPORTATION INFRASTRUCTURE
# Create a dataframe for relevant variables
dim5 = chile_data_s.iloc[:, 69:81].mul(weights['weighted_average'][69:81], axis = 1)
# Create a score ranking
dim5['Ranking 5'] = dim5.sum(axis = 1)
# Sort the dataframe by score
dim5.sort_values(by = 'Ranking 5', ascending = False).head()
Out[111]:
| NATIONAL TOURISTS ARRIVALS | INTERNATIONAL TOURISTS ARRIVALS | NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY | DENSITY OF AIRPORTS | DENSITY OF ROADS AND HIGHWAYS | % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) | NUMBER OF VEHICLES | VISITORS TO PROTECTED SITES | NUMBER OF CRUISES THAT ARRIVE PER YEAR | TOURIST'S ARRIVALS THROUGH BORDER LINES | SECONDARY ROADS (KMS) | NUMBER OF INTERNATIONAL BORDER GATES | Ranking 5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | |||||||||||||
| Metropolitana | 0.100554 | 0.259728 | 0.120251 | -0.006189 | 0.030724 | 0.089124 | 0.215677 | -0.013287 | -0.033058 | 0.255464 | 0.006264 | -0.005095 | 1.020156 |
| Los Lagos | 0.023358 | 0.004555 | 0.002669 | 0.163059 | 0.006485 | -0.006800 | -0.014619 | 0.128954 | 0.110080 | -0.003165 | 0.053512 | 0.003396 | 0.471484 |
| Valparaíso | 0.092360 | 0.005276 | 0.010854 | -0.017268 | 0.041864 | 0.027583 | 0.021942 | 0.008830 | 0.125416 | 0.062767 | -0.011485 | -0.005095 | 0.363045 |
| Biobío | 0.075951 | -0.023888 | -0.033787 | -0.028659 | 0.024675 | 0.055932 | 0.021054 | -0.025144 | -0.033058 | -0.055253 | 0.051458 | -0.005095 | 0.024185 |
| Arica y Parinacota | -0.055501 | -0.022816 | 0.128799 | 0.063833 | 0.001562 | -0.034721 | -0.031189 | -0.037418 | 0.033399 | -0.010150 | -0.021722 | 0.003396 | 0.017473 |
In [112]:
# Ranking for dimension 6
# Create a dataframe for relevant variables
dim6 = chile_data_s.iloc[:, 81:96].mul(weights['weighted_average'][81:96], axis = 1)
# Create a score ranking
dim6['Ranking 6'] = dim6.sum(axis = 1)
# Sort by score
dim6.sort_values(by = 'Ranking 6', ascending = False).head()
Out[112]:
| Density of restaurants and other food services per 100,000 inhabitants | Density of People employed in restaurants and the like per 10,000 inhabitants | Car rental companies | Densidad de camas en hospitales por cada 10.000 habitantes | Density of beds in hospitals per 10,000 inhabitants | Number of spas | Density of gambling casinos per million inhabitants | Number of golf courses | Number of craft centers | Density of tour guides per 100,000 inhabitants | Number of thermal centers | Density of Sports Facilities and Venues per 10,000 inhabitants | Penetration of telephone lines in service per 100 inhabitants | Density of service stations | Number of tour-operator companies certified with the tourism quality seal | Ranking 6 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | ||||||||||||||||
| Metropolitana | -0.002920 | 0.000430 | 0.192329 | 0.000379 | 0.065011 | 0.003205 | 0.010747 | 0.217912 | 0.022473 | -0.000839 | -0.000457 | -0.002207 | 0.133232 | 0.173335 | 0.238759 | 1.051391 |
| Valparaíso | 0.002111 | -0.000557 | -0.001669 | 0.004191 | 0.022296 | -0.004807 | 0.005638 | 0.102547 | 0.025638 | -0.000526 | -0.001028 | -0.000191 | 0.033212 | 0.030820 | -0.013057 | 0.204619 |
| Antofagasta | 0.000345 | -0.000177 | 0.035879 | 0.007929 | 0.081541 | 0.000534 | 0.000127 | -0.012818 | 0.012977 | -0.000177 | -0.001599 | -0.002421 | 0.061526 | -0.030009 | -0.020052 | 0.133605 |
| Magallanes y Antártica | 0.002880 | -0.000959 | -0.026701 | 0.015925 | 0.092045 | 0.003205 | -0.024034 | -0.038455 | -0.020258 | 0.004777 | -0.002741 | -0.000365 | 0.099192 | -0.037830 | -0.013057 | 0.053626 |
| Los Lagos | 0.000248 | 0.000088 | 0.048395 | -0.004477 | -0.015562 | 0.001424 | -0.000233 | -0.025637 | -0.001266 | -0.000156 | 0.001827 | 0.003351 | -0.034224 | -0.009588 | 0.007928 | -0.027883 |
In [113]:
# Ranking for dimension 7
# Create a dataframe for relevant variables
dim7 = chile_data_s.iloc[:, 96:108].mul(weights['weighted_average'][96:108], axis = 1)
# Create a score ranking
dim7['Ranking 7'] = dim7.sum(axis = 1)
# Sort the by score
dim7.sort_values(by = 'Ranking 7', ascending = False).head()
Out[113]:
| Perception of exposure to crime (%) | Percentage of victimized households with at least one victim | Density of homicides per million inhabitants | Density of crimes against public health per million inhabitants | Black figure index | Budget for public safety (Thousands of $) | Percentage of households that reported at least one crime | Number of declared crimes | Number of crimes investigated | Number of accidents (roads, air and waterways) | Illegal commerce | Number of Carabineros | Ranking 7 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | |||||||||||||
| Metropolitana | 0.011710 | 0.008464 | -0.005479 | 0.001525 | 0.013343 | 0.136506 | 0.033718 | 0.213969 | 0.218710 | 0.208089 | 0.213174 | 0.202417 | 1.256147 |
| Biobío | 0.007609 | 0.012892 | -0.005879 | 0.002058 | 0.000601 | 0.064933 | 0.050537 | 0.027434 | 0.047618 | 0.020326 | -0.023744 | 0.038650 | 0.243035 |
| Valparaíso | -0.009136 | 0.007283 | 0.000862 | 0.000485 | 0.003659 | 0.001270 | 0.008490 | 0.015183 | 0.047388 | 0.031594 | -0.019705 | 0.027224 | 0.114596 |
| Tarapacá | 0.028456 | 0.059541 | 0.000922 | 0.002058 | 0.025066 | -0.033773 | 0.064953 | -0.019599 | -0.038989 | -0.029033 | -0.006338 | -0.030142 | 0.023122 |
| Araucanía | 0.014786 | 0.006397 | -0.001826 | 0.001130 | -0.012397 | 0.019406 | -0.020343 | -0.005805 | -0.004950 | -0.007010 | -0.014791 | -0.004805 | -0.030207 |
In [114]:
# Ranking for dimension 8
# Create a dataframe for relevant variables
dim8 = chile_data_s.iloc[:, 108:117].mul(weights['weighted_average'][108:117], axis = 1)
# Create a score ranking
dim8['Ranking 8'] = dim8.sum(axis = 1)
# Sort the dataframe by score
dim8.sort_values(by = 'Ranking 8', ascending = False).head()
Out[114]:
| Unemployment rate | Poverty rate | Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants | Number of strikes carried out | Average (days) duration of a strike | Person-day cost of a strike | Density of Bank Branches per million inhabitants | Floating population | Volume of exports | Ranking 8 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Region | ||||||||||
| Metropolitana | -0.007245 | 0.014005 | -0.002871 | 0.216196 | 0.008502 | 0.187929 | 0.226387 | 0.238420 | 0.016126 | 0.897449 |
| Los Lagos | 0.013886 | -0.003121 | -0.005038 | -0.022959 | 0.021079 | -0.030225 | 0.160958 | -0.022756 | -0.005356 | 0.106469 |
| Antofagasta | 0.000302 | 0.022568 | -0.006617 | 0.003348 | 0.016399 | 0.011980 | -0.016823 | -0.018545 | 0.073141 | 0.085753 |
| Tarapacá | 0.007346 | -0.012254 | 0.057963 | -0.013393 | 0.000897 | 0.056389 | -0.025542 | -0.024169 | 0.000008 | 0.047245 |
| Valparaíso | -0.006239 | -0.001979 | -0.001363 | -0.003826 | 0.013182 | -0.008444 | -0.024362 | 0.029428 | 0.005731 | 0.002128 |
In [115]:
# Ranking for dimension 9
# Create a dataframe for relevant variables
dim9 = chile_data_s.iloc[:, 117:122].mul(weights['weighted_average'][117:122], axis = 1)
# Create a score ranking
dim9['Ranking 9'] = dim9.sum(axis = 1)
# Sort the dataframe by score
dim9.sort_values(by = 'Ranking 9', ascending = False).head()
Out[115]:
| Density of Tourist Information Offices per million inhabitants | Number of visits to Tourist Information Offices | Average monthly global searches by tourist attraction on the internet | National tourism promotion budget (Thousands of USD) | International tourism promotion budget (Thousands of USD) | Ranking 9 | |
|---|---|---|---|---|---|---|
| Region | ||||||
| Metropolitana | 0.003250 | 0.073905 | 0.234107 | 0.027813 | -6.620432e-19 | 0.339075 |
| Coquimbo | 0.001995 | 0.017060 | 0.052264 | -0.004692 | -2.538132e-03 | 0.064088 |
| Antofagasta | -0.000266 | 0.052855 | -0.002598 | -0.007162 | 9.232840e-04 | 0.043753 |
| Los Lagos | -0.000396 | -0.026438 | 0.090275 | -0.022939 | -1.446337e-03 | 0.039057 |
| Araucanía | 0.002475 | 0.006315 | -0.024473 | 0.015141 | -4.604072e-03 | -0.005146 |
In [116]:
# Ranking for dimension 10
# Create a dataframe for relevant variables
dim10 = chile_data_s.iloc[:, 122:127].mul(weights['weighted_average'][122:127], axis = 1)
# Create a score ranking
dim10['Ranking 10'] = dim10.sum(axis = 1)
# Sort the dataframe by score
dim10.sort_values(by = 'Ranking 10', ascending = False).head()
Out[116]:
| Investments in public infrastructure made by the Ministry of Public Works | Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) | Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population | Funds obtained from FNRD (Thousands of pesos) | Number of regional strategic development plans | Ranking 10 | |
|---|---|---|---|---|---|---|
| Region | ||||||
| Metropolitana | 0.170579 | 0.119339 | 0.020648 | 0.096992 | 0.025963 | 0.433521 |
| Los Lagos | 0.029156 | 0.102486 | -0.007826 | 0.033758 | -0.012981 | 0.144593 |
| Biobío | 0.069408 | 0.024954 | -0.046092 | 0.090950 | -0.051925 | 0.087295 |
| Valparaíso | 0.029846 | 0.000385 | -0.010939 | 0.009322 | 0.025963 | 0.054576 |
| Antofagasta | -0.039602 | 0.030116 | 0.015149 | 0.007522 | 0.025963 | 0.039147 |
In [117]:
# Create an aggregated dataframe with all scores
final_scoring_data = pd.concat([dim1.iloc[:,-1:], dim2.iloc[:,-1:], dim3.iloc[:,-1:], dim4.iloc[:,-1:], dim5.iloc[:,-1:],
dim6.iloc[:,-1:], dim7.iloc[:,-1:], dim8.iloc[:,-1:], dim9.iloc[:,-1:],
dim10.iloc[:,-1:]], axis = 1)
# Print
final_scoring_data
Out[117]:
| Ranking 1 | Ranking 2 | Ranking 3 | Ranking 4 | Ranking 5 | Ranking 6 | Ranking 7 | Ranking 8 | Ranking 9 | Ranking 10 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Region | ||||||||||
| Arica y Parinacota | -0.352040 | 0.012614 | -0.200605 | -0.133940 | 0.017473 | -0.112601 | -0.171233 | -0.108058 | -0.062982 | -0.138605 |
| Tarapacá | -0.151878 | -0.064461 | -0.014831 | -0.050027 | -0.111674 | -0.141030 | 0.023122 | 0.047245 | -0.015176 | -0.149593 |
| Antofagasta | 0.057032 | -0.194991 | -0.038854 | -0.000971 | -0.006765 | 0.133605 | -0.074815 | 0.085753 | 0.043753 | 0.039147 |
| Atacama | -0.340943 | -0.335391 | -0.152789 | -0.184250 | -0.352432 | -0.089080 | -0.261799 | -0.117217 | -0.010370 | -0.124519 |
| Coquimbo | -0.274780 | 0.115361 | -0.103491 | 0.052269 | -0.186785 | -0.184503 | -0.132779 | -0.157317 | 0.064088 | -0.017804 |
| Valparaíso | 0.927056 | 0.495811 | 0.228590 | 0.395370 | 0.363045 | 0.204619 | 0.114596 | 0.002128 | -0.008493 | 0.054576 |
| Metropolitana | 2.068241 | 0.351619 | 1.046783 | 0.863165 | 1.020156 | 1.051391 | 1.256147 | 0.897449 | 0.339075 | 0.433521 |
| O'Higgins | -0.401523 | -0.129822 | -0.123471 | -0.286507 | -0.277080 | -0.190150 | -0.190980 | -0.040408 | -0.079938 | -0.044923 |
| Maule | -0.466935 | -0.108885 | -0.217622 | -0.224087 | -0.204509 | -0.259407 | -0.173796 | -0.144189 | -0.062501 | -0.011888 |
| Biobío | 0.227089 | -0.277922 | 0.113008 | -0.094783 | 0.024185 | -0.043009 | 0.243035 | -0.063800 | -0.032632 | 0.087295 |
| Araucanía | -0.368866 | -0.081632 | -0.153601 | 0.009356 | -0.046268 | -0.096631 | -0.030207 | -0.173586 | -0.005146 | 0.037159 |
| Los Ríos | -0.338965 | -0.096590 | -0.230853 | -0.090791 | -0.290327 | -0.153838 | -0.204846 | -0.141987 | -0.065986 | -0.045419 |
| Los Lagos | 0.139753 | 0.324290 | 0.155459 | 0.228063 | 0.471484 | -0.027883 | -0.079900 | 0.106469 | 0.039057 | 0.144593 |
| Aysén | -0.483358 | -0.009676 | -0.197593 | -0.363014 | -0.400063 | -0.145107 | -0.134980 | -0.066532 | -0.046256 | -0.173870 |
| Magallanes y Antártica | -0.239883 | -0.000325 | -0.110129 | -0.119854 | -0.020441 | 0.053626 | -0.181565 | -0.125951 | -0.096493 | -0.089669 |
In [118]:
# Create a list of column names
list_of_my_columns = [final_scoring_data['Ranking 1'],
final_scoring_data['Ranking 2'],
final_scoring_data['Ranking 3'],
final_scoring_data['Ranking 4'],
final_scoring_data['Ranking 5'],
final_scoring_data['Ranking 6'],
final_scoring_data['Ranking 7'],
final_scoring_data['Ranking 8'],
final_scoring_data['Ranking 9'],
final_scoring_data['Ranking 10']]
# Summarize and create an overall ranking
final_scoring_data['Overall Ranking'] = pd.concat(list_of_my_columns, axis = 1).sum(axis = 1)
# Print
final_scoring_data
Out[118]:
| Ranking 1 | Ranking 2 | Ranking 3 | Ranking 4 | Ranking 5 | Ranking 6 | Ranking 7 | Ranking 8 | Ranking 9 | Ranking 10 | Overall Ranking | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | |||||||||||
| Arica y Parinacota | -0.352040 | 0.012614 | -0.200605 | -0.133940 | 0.017473 | -0.112601 | -0.171233 | -0.108058 | -0.062982 | -0.138605 | -1.249977 |
| Tarapacá | -0.151878 | -0.064461 | -0.014831 | -0.050027 | -0.111674 | -0.141030 | 0.023122 | 0.047245 | -0.015176 | -0.149593 | -0.628302 |
| Antofagasta | 0.057032 | -0.194991 | -0.038854 | -0.000971 | -0.006765 | 0.133605 | -0.074815 | 0.085753 | 0.043753 | 0.039147 | 0.042893 |
| Atacama | -0.340943 | -0.335391 | -0.152789 | -0.184250 | -0.352432 | -0.089080 | -0.261799 | -0.117217 | -0.010370 | -0.124519 | -1.968790 |
| Coquimbo | -0.274780 | 0.115361 | -0.103491 | 0.052269 | -0.186785 | -0.184503 | -0.132779 | -0.157317 | 0.064088 | -0.017804 | -0.825742 |
| Valparaíso | 0.927056 | 0.495811 | 0.228590 | 0.395370 | 0.363045 | 0.204619 | 0.114596 | 0.002128 | -0.008493 | 0.054576 | 2.777297 |
| Metropolitana | 2.068241 | 0.351619 | 1.046783 | 0.863165 | 1.020156 | 1.051391 | 1.256147 | 0.897449 | 0.339075 | 0.433521 | 9.327548 |
| O'Higgins | -0.401523 | -0.129822 | -0.123471 | -0.286507 | -0.277080 | -0.190150 | -0.190980 | -0.040408 | -0.079938 | -0.044923 | -1.764803 |
| Maule | -0.466935 | -0.108885 | -0.217622 | -0.224087 | -0.204509 | -0.259407 | -0.173796 | -0.144189 | -0.062501 | -0.011888 | -1.873819 |
| Biobío | 0.227089 | -0.277922 | 0.113008 | -0.094783 | 0.024185 | -0.043009 | 0.243035 | -0.063800 | -0.032632 | 0.087295 | 0.182466 |
| Araucanía | -0.368866 | -0.081632 | -0.153601 | 0.009356 | -0.046268 | -0.096631 | -0.030207 | -0.173586 | -0.005146 | 0.037159 | -0.909421 |
| Los Ríos | -0.338965 | -0.096590 | -0.230853 | -0.090791 | -0.290327 | -0.153838 | -0.204846 | -0.141987 | -0.065986 | -0.045419 | -1.659601 |
| Los Lagos | 0.139753 | 0.324290 | 0.155459 | 0.228063 | 0.471484 | -0.027883 | -0.079900 | 0.106469 | 0.039057 | 0.144593 | 1.501386 |
| Aysén | -0.483358 | -0.009676 | -0.197593 | -0.363014 | -0.400063 | -0.145107 | -0.134980 | -0.066532 | -0.046256 | -0.173870 | -2.020451 |
| Magallanes y Antártica | -0.239883 | -0.000325 | -0.110129 | -0.119854 | -0.020441 | 0.053626 | -0.181565 | -0.125951 | -0.096493 | -0.089669 | -0.930684 |
In [119]:
final_scoring_data.style.highlight_null().render().split('\n')[:10]
def color_negative_red(val):
"""
Takes a scalar and returns a string with
the css property `'color: red'` for negative
strings, black otherwise.
"""
color = 'red' if val < 0 else 'black'
return 'color: %s' % color
def highlight_max(s):
'''
highlight the maximum in a Series yellow.
'''
is_max = s == s.max()
return ['background-color: yellow' if v else '' for v in is_max]
final_scoring_data.style.\
applymap(color_negative_red).\
apply(highlight_max)
Out[119]:
| Ranking 1 | Ranking 2 | Ranking 3 | Ranking 4 | Ranking 5 | Ranking 6 | Ranking 7 | Ranking 8 | Ranking 9 | Ranking 10 | Overall Ranking | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | |||||||||||
| Arica y Parinacota | -0.352040 | 0.012614 | -0.200605 | -0.133940 | 0.017473 | -0.112601 | -0.171233 | -0.108058 | -0.062982 | -0.138605 | -1.249977 |
| Tarapacá | -0.151878 | -0.064461 | -0.014831 | -0.050027 | -0.111674 | -0.141030 | 0.023122 | 0.047245 | -0.015176 | -0.149593 | -0.628302 |
| Antofagasta | 0.057032 | -0.194991 | -0.038854 | -0.000971 | -0.006765 | 0.133605 | -0.074815 | 0.085753 | 0.043753 | 0.039147 | 0.042893 |
| Atacama | -0.340943 | -0.335391 | -0.152789 | -0.184250 | -0.352432 | -0.089080 | -0.261799 | -0.117217 | -0.010370 | -0.124519 | -1.968790 |
| Coquimbo | -0.274780 | 0.115361 | -0.103491 | 0.052269 | -0.186785 | -0.184503 | -0.132779 | -0.157317 | 0.064088 | -0.017804 | -0.825742 |
| Valparaíso | 0.927056 | 0.495811 | 0.228590 | 0.395370 | 0.363045 | 0.204619 | 0.114596 | 0.002128 | -0.008493 | 0.054576 | 2.777297 |
| Metropolitana | 2.068241 | 0.351619 | 1.046783 | 0.863165 | 1.020156 | 1.051391 | 1.256147 | 0.897449 | 0.339075 | 0.433521 | 9.327548 |
| O'Higgins | -0.401523 | -0.129822 | -0.123471 | -0.286507 | -0.277080 | -0.190150 | -0.190980 | -0.040408 | -0.079938 | -0.044923 | -1.764803 |
| Maule | -0.466935 | -0.108885 | -0.217622 | -0.224087 | -0.204509 | -0.259407 | -0.173796 | -0.144189 | -0.062501 | -0.011888 | -1.873819 |
| Biobío | 0.227089 | -0.277922 | 0.113008 | -0.094783 | 0.024185 | -0.043009 | 0.243035 | -0.063800 | -0.032632 | 0.087295 | 0.182466 |
| Araucanía | -0.368866 | -0.081632 | -0.153601 | 0.009356 | -0.046268 | -0.096631 | -0.030207 | -0.173586 | -0.005146 | 0.037159 | -0.909421 |
| Los Ríos | -0.338965 | -0.096590 | -0.230853 | -0.090791 | -0.290327 | -0.153838 | -0.204846 | -0.141987 | -0.065986 | -0.045419 | -1.659601 |
| Los Lagos | 0.139753 | 0.324290 | 0.155459 | 0.228063 | 0.471484 | -0.027883 | -0.079900 | 0.106469 | 0.039057 | 0.144593 | 1.501386 |
| Aysén | -0.483358 | -0.009676 | -0.197593 | -0.363014 | -0.400063 | -0.145107 | -0.134980 | -0.066532 | -0.046256 | -0.173870 | -2.020451 |
| Magallanes y Antártica | -0.239883 | -0.000325 | -0.110129 | -0.119854 | -0.020441 | 0.053626 | -0.181565 | -0.125951 | -0.096493 | -0.089669 | -0.930684 |